Analysis Of Intellectual-Property Data In Relation To Products And Services

ABSTRACT

Techniques described herein are directed to analyzing intellectual-property data according to provide various intellectual property related services to organizations. In particular implementations, information related to products and/or services may be obtained from a number of data sources. Additionally, information related to intellectual-property assets, such as patents, trademarks, copyrights, trade secrets, and know-how, may be obtained. In various situations, the intellectual-property assets may be mapped to respective products and/or services. The mappings between the products and/or services and intellectual-property assets may be used to provide intellectual property related services that correspond to the intellectual-property assets, such as valuation services, strategy-related services, or risk-related services.

BACKGROUND

Intellectual property is obtained by organizations to help protectinnovation within the organizations. Typically, information related tointellectual property of an organization can be difficult to effectivelyand efficiently analyze. For example, understanding the value of theintellectual property or understanding how intellectual property relatesto products or services in the marketplace can be difficult to achievein an accurate and efficient manner using computer-implementedtechniques.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is set forth below with reference to theaccompanying figures. In the figures, the left-most digit(s) of areference number identifies the figure in which the reference numberfirst appears. The use of the same reference numbers in differentfigures indicates similar or identical items. The systems depicted inthe accompanying figures are not to scale and components within thefigures may be depicted not to scale with each other.

FIG. 1 illustrates an example architecture to analyzeintellectual-property data and utilize the analysis of theintellectual-property data to provide a number of services according tosome implementations.

FIG. 2 illustrates an example environment to analyze types ofintellectual-property data and product/service data to provide servicesrelated to intellectual property according to some implementations.

FIG. 3 illustrates an example environment to generate mappings betweenproducts and intellectual-property assets using a technology taxonomyaccording to some implementations.

FIG. 4 illustrates an example system to generate valuations forintellectual-property assets according to some implementations.

FIG. 5 illustrates an example system to modify mappings betweenintellectual property and taxonomy classifications and and/or betweenintellectual property and products/services according to someimplementations.

FIG. 6 illustrates an example architecture to provide services tocustomers using mappings between intellectual property andproducts/services in relation to a classification system according tosome implementations.

FIG. 7 illustrates an example framework to generate linguisticstructures for claims of patent documents according to someimplementations.

FIG. 8 illustrates an example framework to determine a similarity metricbetween a linguistic structure for a portion of a claim of a patentdocument and a linguistic structure of a product/service according tosome implementations.

FIG. 9 illustrates an example framework to determine a value of anintellectual-property feature that corresponds to one or more productsaccording to some implementations.

FIG. 10 illustrates an example process to determine anintellectual-property asset that corresponds to a product and/or serviceaccording to some implementations.

FIG. 11 illustrates an example process to determine anintellectual-property asset that corresponds to a product or serviceusing a classification system according to some implementations.

FIG. 12 illustrates an example process to perform a qualitative analysisand a quantitative analysis of intellectual-property data according tosome implementations.

FIG. 13 illustrates an example process to determine anintellectual-property asset that corresponds to a product and/or serviceusing a linguistic structure of the intellectual-property asset and alinguistic structure of the product and/or service according to someimplementations.

FIG. 14 illustrates an example process to provide services to a customerbased on relationships between a product and/or service and anintellectual-property asset according to some implementations.

DETAILED DESCRIPTION

Techniques described herein are directed to analyzingintellectual-property data in relation to products and/or services. Astechnological advancement has increased, and the value of organizationshas been characterized by the shift from tangible assets to intangibleassets, the importance of intellectual property has also increased.Thus, organizations have taken various measures to safeguard theirintellectual property, which may include patents, trademarks,copyrights, trade secrets, and/or know-how, for example. However, therehave been few techniques, architectures, and frameworks developed toanalyze intellectual-property data and generate useful information froman organization's intellectual-property data. Additionally, the numberof services provided to organizations using intellectual property isalso limited due to the complexity of analyzing intellectual-propertydata and the inability of conventional systems to effectively provideinformation to organizations regarding their intellectual property thatis of value to the organizations.

The implementations described herein are directed to techniques,systems, and architectures to analyze intellectual-property data togenerate frameworks that may be used to provide services related tointellectual-property assets. In particular implementations, anintellectual-property services provider may obtain intellectual-propertydata from a number of data sources. In various implementations, at leasta portion of the data sources may include public data sources. Publicdata sources storing intellectual-property data may include databases ofpatent offices of various jurisdictions, such as the Unites StatesPatent and Trademark Office (USPTO) database, the European Patent Office(EPO) database, and/or the World Intellectual Property Office (WIPO)database. Additionally, intellectual-property data may be stored indatabases related to copyrights, such as the United States CopyrightOffice or the European Union Copyright Office. The intellectual-propertydata may also be obtained from private data sources. The private datasources may include databases that store information related to anorganization that are maintained and/or controlled by the organization.The private data sources may also include databases of service providersthat store information on behalf of an organization. Further, at least aportion of the intellectual-property data of an organization may becaptured via one or more user interfaces. In some situations, the one ormore user interfaces may be rendered as part of customer portals thatare accessible to customers of the intellectual-property servicesprovider. In examples, the data sources may include a digital-propertyregistry, which may be maintained and/or generated by a system and/orentity other than the organization. For example, a digital property,such as a trade secret, may be registered with the digital-propertyregistry utilizing one or more obfuscation values to represent thedigital property and/or block values to represent a block in adistributed ledger where the obfuscation value is registered.

The intellectual-property services provider may also obtain data relatedto a number of products and/or services. The products and/or servicesmay be offered for acquisition by a same organization for which theintellectual-property data is being obtained and analyzed. Additionally,the products and/or services may be offered for acquisition byorganizations differing from an organization for which theintellectual-property data is being obtained and analyzed. The datarelated to the products and/or services may include at least one ofeconomic data related to the products and/or services, manuals regardingthe products and/or services, specification sheets for the productsand/or services, descriptions of the products and/or services, and/ormarketing materials related to the products and/or services.

The data related to products and/or services may be obtained from anumber of data sources. In particular implementations, the data relatedto products and/or services may be obtained from various websites. Insome scenarios, the data related to the products and/or services may beobtained from one or more websites of organizations that are offeringthe products and/or services for acquisition. In additionalimplementations, the data related to the products and/or services may beobtained from databases of the organizations offering the productsand/or services for acquisition. Further, the data related to theproducts and/or services may be obtained via one or more userinterfaces, such as user interfaces provided by theintellectual-property services provider as part of a customer portal.

Data related to intellectual property of an organization and datarelated to products and/or services may also be obtained throughcrowdsourcing. In particular implementations, the intellectual-propertyservices provider may publish requests for information aboutintellectual-property assets and/or requests for information aboutproducts and/or services. The requests may be published on one or morewebsites, via one or more mobile device applications, sent to a group ofindividuals, or combinations thereof. In response to the requests,individuals may identify information that corresponds to the request andsend the information to the intellectual-property services provider.

After obtaining information about products and/or services and obtainingintellectual-property information, an intellectual-property servicesprovider may analyze the information and organize the information insuch a way that the intellectual-property services provider may providea number of services to customers of the intellectual-property servicesprovider. The intellectual-property services provider may analyze theinformation obtained from the data sources using machine learningtechniques. In particular implementations, the intellectual-propertyservices provider may generate one or more models that may be utilizedto determine properties, characteristics, metrics, and the like withrespect to intellectual-property assets and products and/or services. Invarious implementations, the intellectual-property services provider mayimplement machine learning techniques to determine relationships betweenintellectual-property assets and products and/or services. In someexamples, the intellectual-property services provider may utilize therelationships between intellectual-property assets and products and/orservices to estimate the value of intellectual-property assets. Theintellectual-property services provider may also utilize machinelearning techniques to determine levels of exposure corresponding tointellectual-property assets. The levels of exposure associated with theintellectual-property assets may correspond to a probability that atleast one of coverage of the intellectual-property assets may decreaseor that a litigation event with respect to the intellectual-propertyassets occurs.

The intellectual-property services provider may utilize natural languageprocessing techniques in order to analyze the information obtained fromthe data sources related to the intellectual-property assets and theproducts and/or services. To illustrate, the intellectual-propertyservices provider may parse words included in information associatedwith products and/or services and information associated with theintellectual-property assets and determine parts of speech for thewords. In certain examples, the intellectual-property services providermay determine relationships between words using the parts of speech ofthe words and grammatical relationships between the words. Theintellectual-property services provider may utilize natural languageprocessing techniques and/or machine learning techniques to alsodetermine relationships between products and/or services andintellectual-property assets. That is, the intellectual-propertyservices provider may utilize natural language processing techniques todetermine intellectual-property documents that may cover one or morefeatures of the products and/or services. In illustrative examples, theintellectual-property services provider may utilize natural languageprocessing techniques and machine learning techniques to determineprobabilities that intellectual-property assets may be enforced withrespect to corresponding products and/or services.

In particular implementations, the intellectual-property servicesprovider may generate linguistic structures that correlate tointellectual-property documents using natural language processingtechniques and/or machine learning techniques to determine relationshipsbetween words included in the information related to theintellectual-property assets. For example, the intellectual-propertyservices provider may determine verbs related to actions performed in aclaim of a patent document and also determine nouns and/or adjectivesthat correspond to the actions. In some situations, theintellectual-property services provider may utilize natural languageprocessing techniques and machine learning techniques to determineelements of claims of patent documents. In addition, theintellectual-property services provider may generate linguisticstructures for products and/or services using natural languageprocessing techniques and machine learning techniques. In illustrativeexamples, the intellectual-property services provider may determineactions performed with respect to products and/or services and generatelinguistic structures that indicate verbs related to the actions andnouns, adjectives, and/or adverbs that are related to the verbs. Invarious implementations, the intellectual-property services provider maydetermine intellectual-property assets that correspond to variousproducts and/or services by comparing the respective linguisticstructures of the intellectual-property assets and the products and/orservices.

The intellectual-property services provider may determineintellectual-property assets that correspond with features of productsand/or services using a technology-classification framework. Thetechnology-classification framework may include a taxonomy that includesa number of classifications with each of the classifications beingassociated with a number of criteria. Classifications forintellectual-property documents may be determined according to thetechnology-classification framework by performing a linguistic analysisof the intellectual-property documents and determining features of theintellectual-property documents. The intellectual-property servicesprovider may then compare the features of the intellectual-propertydocuments against the criteria for the classifications of thetechnology-classification framework to determine respectiveclassifications for the intellectual-property documents. Additionally,the intellectual-property services provider may also determineclassifications for products and/or services according to thetechnology-classification system. For example, the intellectual-propertyservices provider may perform a linguistic analysis of informationrelated to products and/or services and determine features of theproducts and/or services. The intellectual-property service provider maythen compare features of the products and/or services in relation tocriteria for the classifications of the technology-classificationframework to determine respective classifications for the productsand/or service. In particular implementations, the intellectual-propertyservices provider may determine intellectual-property assets thatcorrespond to products and/or services when the intellectual-propertyassets and the products and/or services are in a same or similarclassification of the technology-classification framework.

In illustrative implementations, the intellectual-property servicesprovider may generate one or more models that map products and/orservices to a technology-classification framework and that mapintellectual-property assets to the technology-classification framework.The intellectual-property services provider may then utilize naturallanguage processing techniques and/or machine learning techniques tofurther develop the one or more models by determiningintellectual-property assets that correspond to various products and/orservices within a given classification. In this way, theintellectual-property services provider may receive requests to identifyintellectual-property assets corresponding to a specified product and/orservice and utilize the one or more models to identify theintellectual-property assets that correspond to the specified productand/or service. The intellectual-property services provider may thendetermine valuations for the intellectual-property assets based at leastpartly on revenue of the specified product and/or service. For example,the intellectual-property services provider may determine a portion ofthe revenue of a particular product and/or service that is attributableto an intellectual-property asset and estimate a value of theintellectual-property asset based at least in part on the portion ofrevenue of the product and/or service attributable to theintellectual-property asset. The intellectual-property services providercan also utilize the one or more models and thetechnology-classification framework to provide additional information tocustomers. To illustrate, the intellectual-property services providermay utilize the one or more models and the technology-classificationframework to determine an amount of exposure and/or loss with respect tointellectual-property assets. The intellectual-property servicesprovider may also provide services to customers using the one or moremodels and the technology-classification framework related to providingmetrics for a portfolio of intellectual-property assets of anorganization. The metrics may indicate measures of breadth and coveragewith respect to the intellectual-property documents. Theintellectual-property services provider may also generate reports usingthe one or more models and the technology-classification frameworkindicating technology features around which an organization may acquireand/or develop additional intellectual-property assets. Additionally,the intellectual-property services provider may generate reports usingthe one or more models and the technology-classification systemindicating intellectual-property assets of competitors of customers ofthe intellectual-property services provider and/or indicating metrics ofthe intellectual-property assets of competitors of customers of theintellectual-property services provider.

Conventional techniques and systems that analyze intellectual-propertydocuments with respect to products and/or services are performed byindividuals with the use of computers. For example, the individuals mayperform manual searches of intellectual-property databases and onlinesearches to identify information about products and/or services. Theindividuals may then perform a manual analysis to determineintellectual-property documents that correspond to products and/orservices. In certain situations, individuals may also access onlineresources related to the sale of intellectual-property assets,litigation verdicts for intellectual-property assets, and/or settlementagreements for litigation proceedings with respect tointellectual-property assets to determine the value of one or moreintellectual-property assets.

However, the conventional techniques and systems used to determinerelationships between intellectual-property assets and various productsand/or services and to determine valuations for intellectual-propertyassets are inefficient and, often, inaccurate. To illustrate,individuals are often unable to search and retrieve large amounts ofdata relating to intellectual-property assets and products and/orservices. Typically, information is overlooked or not found in manualsearches performed online by individuals, where the information may beuseful in identifying intellectual-property assets that correspond torespective goods and/or services and in determining valuations ofintellectual-property assets. Further, a human-based analysis of theinformation collected may often miss relationships betweenintellectual-property assets and products and/or services or may missfeatures covered by intellectual-property assets that correspond tovarious products and/or services. Thus, conventional techniques andsystems are labor intensive and often do not provide information that isusable by organizations to evaluate the intellectual-property assets ofthe organizations.

Additionally, implementing the techniques and systems described hereinis more than simply collecting and organizing large amounts of data. Thesystems and techniques described herein not only provide usefulinformation regarding intellectual-property assets that correspond toproducts and/or services in a more efficient way with respect toconventional techniques and systems, but the implementations describedherein also utilize techniques and systems that generate accurateinformation that is supported by an analytical basis formed from theunconventional use of machine learning and natural language processing.

The present disclosure provides an overall understanding of theprinciples of the structure, function, manufacture, and use of thesystems and methods disclosed herein. One or more examples of thepresent disclosure are illustrated in the accompanying drawings. Thoseof ordinary skill in the art will understand that the systems andmethods specifically described herein and illustrated in theaccompanying drawings are non-limiting embodiments. The featuresillustrated or described in connection with one embodiment may becombined with the features of other embodiments, including as betweensystems and methods. Such modifications and variations are intended tobe included within the scope of the appended claims.

Additional details are described below with reference to several exampleembodiments.

FIG. 1 illustrates an example architecture 100 to analyzeintellectual-property data and utilize the analysis of theintellectual-property data to provide a number of services according tosome implementations. The architecture 100 may include anintellectual-property services system 102 that analyzes data related tointellectual-property assets. The data analyzed by theintellectual-property services system 102 may be used by anintellectual-property services provider to provide services related tointellectual-property assets. The intellectual-property assets mayinclude patents, trademarks, copyrights, trade secrets, and know-how. Invarious implementations, the intellectual-property assets may include aportion of a patent, such as a claim of a patent. Additionally, theintellectual-property assets may include a portion of a copyright thatis directed to a portion of software code that corresponds to aparticular feature that is performed when the software code is executed.

In particular implementations, intellectual-property assets may beassociated with various forms of documentation that indicate features ofthe intellectual-property assets. In situations where theintellectual-property assets include patents, the patents may includeutility patents, design patents, and/or plant patents. The patents mayalso include patent applications, such as provisional patentapplications, utility patent applications, design patent applications,plant patent applications, or combinations thereof. In variousscenarios, the intellectual-property assets may include trademarkapplications and granted trademark registrations. Theintellectual-property assets may also include documentationcorresponding to copyright registrations and documentation includingaspects of trade secrets. To illustrate, formulas, processes, and/oralgorithms and software code that are the subject of trade secrets maybe documented. Actions taken to preserve the secrecy of trade secretsmay also be documented and included in the intellectual-property assets.In addition, the intellectual-property assets may include documentationof know-how of the organization, such as process improvements andinnovations, new product designs, product improvements, brand names,logos, ad slogans, website design, product appearance, productpackaging, manufacturing processes, engineering drawings, instructionmanuals, product catalogs, customer and supplier lists, and so forth.

The intellectual-property services system 102 may include anintellectual-property mapping and learning system 104. Theintellectual-property mapping and learning system 104 may obtaininformation from a number of data sources, such as data sources 106, andanalyze the information to determine relationships betweenintellectual-property assets and products and/or services. The datasources 106 may include customer portals 108. The customer portals 108may include one or more user interfaces generated by theintellectual-property services system 102 that include one or more userinterface elements to capture information related tointellectual-property assets of customers of an intellectual-propertyservices provider, such as a customer 110. The user interfacesassociated with the customer portals 108 may be displayed as part of oneor more websites of an intellectual-property services provider and/orvia one or more mobile device applications of the intellectual-propertyservices provider. In various implementations, information may beentered into the customer portals 108 by a representative of thecustomer 110. In additional implementations, information may be enteredinto the customer portals 108 by representatives of anintellectual-property services provider.

The data sources 106 may also include one or more customer data sources112. The one or more customer data sources 112 may be accessible to thecustomers of the intellectual-property services provider and store dataunder the direction of the customers of the intellectual-propertyservices provider. That is, the data stored by the one or more customerdata sources 112 may be under the control of respective customers of theintellectual-property services provider. In some illustrative examples,at least one customer data source 112 may be maintained on premises ofthe customer 110. In additional illustrative examples, at least onecustomer data source 112 may be maintained by an additionalorganization, such as an organization that provides remote data storageservices. For example, a customer data source 112 may include acloud-based data storage system that is accessible by the customer 110.

Additionally, the data sources 106 may include crowdsourcing datasources 114. The crowdsourcing data sources 114 may include a number ofindividuals that provide information to the intellectual-propertyservices system 102. In various implementations, theintellectual-property services system 102 may publish requests forinformation about intellectual-property assets via at least one of oneor more websites or one or more mobile device applications. Theintellectual-property service system 102 may also publish requests forinformation about products and/or services that may correspond tointellectual-property assets. In various implementations, individualsincluded in the crowdsourcing data sources 114 may access the requestspublished by the intellectual-property services system 102 using atleast one computing device and provide responses to the requests. Theresponses may include information about at least one ofintellectual-property assets or products and/or services that were thesubject of the requests.

Further, the data sources 106 may include one or more public datasources 116. The one or more public data sources 116 may include datasources that store data that is accessible to the general public. Insome implementations, the one or more public data sources 106 may storedata that is accessible to individuals without any credentials. Inadditional implementations, the one or more public data sources 106 maystore data that is accessible to individuals with credentials that aremade available to the public by organizations maintaining the one ormore public data sources 116. The data sources 116 that store datarelated to intellectual-property assets may be accessible via one ormore websites and/or one or more mobile device applications.

The one or more public data sources 116 may include data sources thatstore data related to intellectual-property assets. For example, the oneor more public data sources 116 may include intellectual propertyorganizations of various governmental jurisdictions, such as the UnitedStates Patent and Trademark Office, the European Patent Office, theWorld Intellectual Property Organization, or the Japanese Patent Office.The intellectual-property data stored by the one or more public datasources 116 may include content of intellectual-property documents. Forexample, the intellectual-property data may include informationcontained in patent documents, such as claims, drawings, backgrounds,abstracts, descriptions of drawings, and the like. In other examples,the intellectual-property data may include content of trademarkdocuments, such as descriptions of goods and services and/orclassifications of goods and services. Additionally, theintellectual-property data may include information included in copyrightdocuments. Further, the intellectual-property data may includeinformation related to the examination of intellectual-propertydocuments. To illustrate, the intellectual-property data may includeprosecution histories of patent applications and/or prosecutionhistories of trademark applications. The intellectual-property data mayalso include bibliographic information related to intellectual-propertydocuments, such as classification of patent documents, examinersassigned to examine patent and trademark applications, priority dates,filing dates, assignees, inventors, applicants, combinations thereof,and the like. In various implementations, the intellectual-property datamay include data related to at least one of administrative proceedings,litigation proceedings, settlement information, or licensing informationfor intellectual-property assets.

The one or more public data sources 116 may also include data sourcesthat store market and financial data. The market and financial data maybe related to organizations offering products and/or services foracquisition. For example, the market and financial data may includefinancial performance of organizations over a period of time.Additionally, the market and financial data may also indicateclassifications and industries for certain organizations. The market andfinancial data may also include financial performance of one or moreindustries over a period of time. Further, the market and financial datamay include data for financial markets, such as stock markets, overtime.

In addition, the one or more public data sources 116 may include datasources that store information about products and/or services. Toillustrate, the one or more public data sources 116 may store data thatincludes descriptions of products and/or services, specifications forproducts, features of products and/or services, images of products,videos related to products and/or services, pricing of products and/orservices, organizations that provide products and/or services,combinations thereof, and so forth.

In particular implementations, the intellectual-property services system102 may include a data acquisition system 118 to obtain data from thedata sources 106. In various implementations, the data acquisitionsystem 118 may extract information from a number of websites. Forexample, the data acquisition system 118 may include one or more webcrawlers that access websites and search for information thatcorresponds to a given set of criteria and extracts the information fromthe websites that correspond to the criteria. In illustrative examples,the data acquisition system 118 may obtain data from the one or moredata sources 106 corresponding to various products and/or services.Additionally, the data acquisition system 118 may obtain data from theone or more data sources 106 corresponding to a number ofintellectual-property assets.

Further, the data acquisition system 118 may perform one or moreoperations with respect to the data obtained from the one or more datasources 106 before the data is stored by the intellectual-propertyknowledge data store 120. For example, the data acquisition system 118may perform optical character recognition operations with respect to atleast a portion of the data obtained from the one or more data sources106. In other examples, the data acquisition system 118 may removeinformation embedded in certain forms of data obtained from the one ormore data sources 106, such as embedded scripts or fonts. The dataacquisition system 118 may also add information to data obtained fromthe one or more data sources 106. To illustrate, the data acquisitionsystem 118 may add time stamps to data obtained from the one or moredata sources 106. The data acquisition system 118 may also add one ormore tags to data obtained from the one or more data sources 106. Theone or more tags may be related to at least one of one or moreorganizations that correspond to the extracted data, one or moretechnology classifications utilized by the intellectual-propertyservices system 102, or one or more categories of intellectual-propertyassets (e.g., patents, trademarks, copyrights, trade secrets, know-how).Additionally, the data acquisition system 118 may apply tags to dataobtained from the one or more data sources 106 indicating that the datais economic data, market data, financial data, product and/or servicedescription data, litigation related data, licensing related data,combinations thereof, and so forth. By applying tags to data obtainedfrom the one or more data sources 106, the data acquisition system 118may store the data in the intellectual-property knowledge data store 120in such a way that the data may be retrieved and analyzed efficiently.

The intellectual-property mapping and learning system 104 may utilizenatural language processing techniques and machine learning techniquesto identify relationships between intellectual-property assets andproducts and/or services. The intellectual-property mapping and learningsystem 104 may also generate data for providing intellectual propertycustomer services 126 to customers of an intellectual-property servicesprovider, such as the customer 110. In particular implementations, theintellectual-property mapping and learning system 104 may include alanguage analysis system 122. The language analysis system 122 mayanalyze words included in information obtained from the one or more datasources 106 to determine parts of speech of the words. For example, thelanguage analysis system 122 may determine that words included ininformation obtained from the one or more data sources 106 may be nouns,verbs, adverbs, adjectives, pronouns, articles, prepositions,conjunctions, and so forth. The language analysis system 122 may alsodetermine relationships between words. To illustrate, the languageanalysis system 122 may identify nouns and adjectives that modify thenouns in addition to verbs and adverbs that modify the verbs. Further,the language analysis system 122 may determine nouns and/or pronounsthat are performing actions corresponding to verbs.

In various implementations, the language analysis system 122 may analyzeinformation obtained from the one or more data sources 106 to identifyportions of intellectual-property documents. For example, the languageanalysis system 122 may analyze a patent document to identify at leastone of a claims portion of the patent document, a detailed descriptionof the patent document, a background of the patent document, a summaryof the patent document, an abstract of the patent document, and soforth. Additionally, the language analysis system 122 may determineindividual elements of claims included in patent documents. Inparticular implementations, the language analysis system 122 maydetermine features included in claims that may be directed to physicalfeatures of a device or system. In various implementations, the featuresmay be directed to actions that are being performed in relation to themethods or processes or actions performed by devices or systems.Further, in scenarios where the claims are directed to compositions ofmatter that correspond to molecules, the features may be directed tovarious arrangements of atoms included in the compositions of matter,such as a phenyl functional group or a carboxyl functional group. Insome situations, the elements of a claim may include a number ofindividual features. In additional examples, the language analysissystem 122 may also analyze a trademark document to identify at leastone of a description of goods and services or international class of thetrademark.

In certain implementations, the language analysis system 122 may analyzeintellectual-property documents obtained from the one or more datasources 106 and generate modified intellectual-property documents. Thelanguage analysis system 122 may generate the modifiedintellectual-property documents by removing portions of the originalintellectual-property documents. For example, the language analysissystem 122 may remove at least one of conjunctions or articles fromintellectual-property documents. In additional examples, the languageanalysis system 122 may generate the modified intellectual-propertydocuments by indicating parts of speech and/or relationships betweenwords in the original intellectual-property documents.

Additionally, the language analysis system 122 may analyze informationrelated to products and/or services and determine features of theproducts and/or services. To illustrate, the language analysis system122 may determine physical components of devices and/or systems. Thelanguage analysis system 122 may also determine technical features ofdevices and/or systems. Further, the language analysis system 122 mayalso determine features of processes and/or methods performed inrelation to products and/or services.

In particular implementations, the language analysis system 122 maydetermine at least one of features of intellectual-property assets,features of products, or features of services by analyzing words relatedto intellectual-property assets, products, and/or services with respectto a library of words related to features of intellectual-propertyassets, products, and/or services. For example, theintellectual-property mapping and learning system 104 may determine aparticular set of words that are related to each of a number ofindividual features that may be associated with at least one of anintellectual-property document, a product, or a service. To illustrate,the intellectual-property mapping and learning system 104 may determinethat words, such as “screen”, “panel”, and “display” may indicate adisplay device feature of an electronic device. Continuing with thisexample, the language analysis system 122 may parseintellectual-property documents and/or information about products and/orservices to identify words that correspond to the words associated witha display device feature. In situations where at least a thresholdnumber of words included in the intellectual-property documents and/orthe information about products and/or services corresponds to the wordsassociated with the display device feature, the language analysis system122 may determine that a particular intellectual-property document or aparticular product and/or service includes the display device feature.

In various implementations, the language analysis system 122 may alsodetermine that proximity between words associated with a feature mayindicate that a feature is present in an intellectual-property documentor in information about a product and/or service. In some examples, whena number of words associated with a feature are within 3 words, within 5words, within 10 words, or within 20 words of each other, the languageanalysis system 122 may determine that the feature is included in anintellectual-property document or in a product and/or service. Inadditional examples, when a number of words associated with a featureare within a same sentence or within a same paragraph, the languageanalysis system 122 may determine that the feature is included in anintellectual-property document or in a product and/or service.

The language analysis system 122 may also generate linguistic structuresfor intellectual-property documents and linguistic features forinformation related to products and/or services. In illustrativeexamples, the language analysis system 122 may generate linguisticstructures for claims of patent documents. In particular scenarios, thelanguage analysis system 122 may generate linguistic structures forelements of claims of patent documents or features of claims of patentdocuments. For example, the language analysis system 122 may identify averb that corresponds to an action of an element of a claim of a patentdocument. The language analysis system 122 may also determine one ormore nouns related to the verb and, in some situations, one or moreadjectives that correspond to the one or more nouns. The languageanalysis system 122 may then generate a linguistic structure that showsrelationships between the verb, the one or more nouns, and/or the one ormore adjectives. Additionally, the language analysis system 122 maygenerate linguistic structures that correspond to actions performed withrespect to products and/or services offered by an organization foracquisition. In particular implementations, the linguistic structure mayinclude a tree structure with a single node as an initial node or rootnode at the top of the tree structure and subsequent nodes branchingfrom the root node. The root node may include a verb that corresponds toan action and the branch nodes may correspond to nouns related to theverb, adjectives related to the nouns, other words related to the verband/or nouns, or combinations thereof.

Additionally, the intellectual-property mapping and learning system 104may include an intellectual-property (IP) model development system 124that determines relationships between intellectual-property documentsand products and/or services. In various implementations, the IPknowledge model development system 124 may identifyintellectual-property assets that correspond to respective productsand/or services. For example, the IP knowledge model development system124 may identify one or more patent claims, an element of a patentclaim, and/or a feature of a patent claim that corresponds to at least aportion of a product and/or service. In additional examples, the IPknowledge model development system 124 may identify a trademark thatcorresponds to a product and/or a service, at least a portion of acopyright that corresponds to a product and/or service, or at leastportion of a trade secret that corresponds to a product and/or service.

The IP knowledge model development system 124 may determine that anintellectual-property asset corresponds to a product and/or service bycomparing linguistic structures of intellectual-property assets withlinguistic structures of products and/or services. In particularimplementations, the IP knowledge model development system 124 maygenerate a first linguistic structure for a feature of anintellectual-property asset and a second linguistic structure for afeature of a product and/or service. The IP knowledge model developmentsystem 124 may compare the first linguistic structure with the secondlinguistic structure to determine a similarity metric between the firstlinguistic structure and the second linguistic structure. In scenarioswhere the similarity metric between the first linguistic structure andthe second linguistic structure is at least a threshold similaritymetric, the IP knowledge model development system 124 may determine thatthe feature of the intellectual-property asset corresponds to thefeature of the product and/or service.

The similarity metric may be based at least partly on words included inthe first linguistic structure and words included in the secondlinguistic structure. The similarity metric may also be based at leastpartly on relationships between words included in the first linguisticstructure and words included in the second linguistic structure. Inillustrative implementations, the first linguistic structure may includea first tree structure with a root node and a number of branch nodesarranged in a first configuration and the second linguistic structuremay include a second tree structure with a root node and an additionalnumber of branch nodes. In these situations, the IP knowledge modeldevelopment system 124 may compare the first tree structure and thesecond tree structure to determine the similarity metric between thefirst linguistic structure and the second linguistic structure. Toillustrate, the IP knowledge model development system 124 may comparewords included in the nodes of the first tree structure and wordsincluded in the nodes of the second tree structure to determine at leasta portion of the similarity metric for the first linguistic structureand the second linguistic structure. Additionally, the IP knowledgemodel development system 124 may compare the first configuration of thefirst tree structure with the second configuration of the second treestructure to determine at least a portion of the similarity metric forthe first linguistic structure and the second linguistic structure. Invarious implementations, the IP knowledge model development system 124may compare the locations of words and/or locations of nodes within thefirst tree structure and the second tree structure to determine asimilarity metric between the first linguistic structure and the secondlinguistic structure.

The IP knowledge model development system 124 may also determinerelationships between intellectual-property assets and products and/orservices using a classification system. The classification system mayinclude a number of classifications with individual classificationshaving one or more criteria to identify intellectual-property assets,products, and/or services to include in the respective classifications.In various implementations, the classifications of the classificationsystem may include a number of technology groups. The classificationsystem may be generated by the intellectual-property mapping andlearning system 104, in some examples. In additional examples, theclassification system may be generated by another entity, such as agovernmental entity, an educational institution, anon-profitorganization, a for-profit organization, or combinations thereof. Inparticular implementations, the IP knowledge model development system124 may compare features of individual intellectual-property assets withcriteria of a number of classifications included in the classificationsystem and determine one or more classifications to associate with theintellectual-property assets. Additionally, the IP knowledge modeldevelopment system 124 may compare features of products and/or serviceswith criteria of a number of classifications of the classificationsystem and determine one or more classifications to associate with theproducts and/or services.

In particular implementations, the IP knowledge model development system124 may determine intellectual-property assets and products and/orservices included in a same classification of the classification system.The IP knowledge model development system 124 may then determine one ormore relationships between intellectual-property assets and productsand/or services included in the same classification of theclassification system. In this way, the IP knowledge model developmentsystem 124 may develop one or more models indicatingintellectual-property assets that correspond to products and/or serviceswithin a classification of the classification system. In an illustrativeexample, the IP knowledge model development system 124 may develop amodel to determine patent claims that correspond to display features ofmobile devices. In another illustrative example, the IP knowledge modeldevelopment system 124 may develop a model to determine trademarks thatcorrespond to fitness tracker devices. In various implementations, theclassification system(s), the relationships betweenintellectual-property assets and products and/or services, and themodels used to determine intellectual-property assets that may berelated to particular products and/or services may be stored by theintellectual-property knowledge data store 120.

The relationships determined by the IP knowledge model developmentsystem 124 between products and/or services and intellectual-propertyassets within particular classifications and the models developed by theIP knowledge model development system 124 to determineintellectual-property assets that correspond with products and/orservices within a classification of the classification system may beused to provide a number of intellectual property customer services 126.The intellectual-property services 126 may include IP strategy-relatedservices 128, IP exposure-related services 130, and IP valuationservices 132. In various implementations, the intellectual propertycustomer services 126 may be provided based on requests sent to theintellectual-property services system 102 for information regarding oneor more intellectual-property assets or one or more products and/orservices. The intellectual-property services system 102 may then utilizethe models, frameworks, and/or relationships betweenintellectual-property assets and products and/or services generated bythe intellectual-property mapping and learning system 104 to respond tothe requests. The requests may be sent, in some situations, byindividuals associated with an intellectual-property services provider,while in additional situations, the request may be sent by individualsassociated with one or more customers 110.

The intellectual property customer services 126 may include intellectualproperty (IP) strategy-related services 128. The IP strategy-relatedservices 128 may include analysis of groups of intellectual-propertyassets. In examples, the IP strategy-related services 128 may includecompetitive landscaping 150, IP benchmarking 152, IP scoring & rating154, an intelligence portfolio tool 156, an IP trend analyzer 158, IPpruning and/or divestiture 160, executive reporting 162, and/orstrategic acquisition 164. In particular implementations, the IPstrategy-related services 128 may include the analysis of a portfolio ofintellectual-property assets of an organization, such as the analysis ofa portfolio of intellectual-property assets of the customer 110. Inillustrative examples, the IP strategy-related services 128 may includeanalyzing a portfolio of patent documents and/or analyzing a portfolioof trademark documents. In various implementations, the IPstrategy-related services 128 may include analyzing a portfolio ofintellectual-property documents of competitors of the customer 110, suchas by using the competitive landscaping 150. For example, theintellectual-property services system 102 may determine technologyclassifications for intellectual-property assets of a competitor of thecustomer 110 and generate one or more documents or a report thatprovides a landscape analysis showing the intellectual-propertydocuments of the competitor with respect to individual technologyclassifications. In some instances, the intellectual-property assets ofthe customer 110 may be mapped against the intellectual-property assetsof a competitor of the customer 110 with regard to their respectivetechnology classifications.

In other examples, the IP strategy-related services 128 may includedetermining scores and/or ratings of intellectual-property assets, suchas by the IP scoring and rating component 154. To illustrate, theintellectual-property services system 102 may determine measures ofbreadth and/or measures of coverage of intellectual-property assets ofthe customer 110 or intellectual-property assets of anotherorganization. The intellectual-property services system 102 may thenrank the intellectual-property assets based on the measures of breadthand/or measures of coverage. The IP strategy-related services 128 mayalso include identifying technology areas in which the customer 110 maywant to develop intellectual-property assets, such as by using the IPbenchmarking component 152. For example, the intellectual-propertyservices system 102 may determine technology classifications in whichthe customer 110 has few or no intellectual-property assets, but arerelated to technology areas that are being developed by the customer110. Additionally, the intellectual-property services system 102 mayidentify future areas of research and development for the customer 110,such as by using the IP portfolio tool 156, based on a number ofintellectual-property assets of the customer and/or a number ofintellectual-property assets of one or more competitors of the customer110 in certain technology areas.

Further, the IP strategy-related services 128 may include identifyingintellectual-property assets of the customer to offer for sale orlicense to other organizations. The intellectual-property service system102 may also generate recommendations for intellectual-property assetsof the customer 110 that may be abandoned or no longer maintained, suchas by the IP pruning and/or divestiture component 160. In particularimplementations, the intellectual-property services system 102 maydetermine at least one of measures of value, measures of breadth, ormeasures of coverage for at least a portion of the intellectual-propertyassets of the customer 110 and utilize the respective measures togenerate recommendations, such as via the executive reporting component162, regarding at least one of sales opportunities, licensingopportunities, or cost savings opportunities (e.g., abandonment) of oneor more intellectual-property assets of the customer 110. Theintellectual-property services system 102 may also determine potentialorganizations and/or intellectual-property assets that may be acquired,such as by the strategic component 164, by the customer based on atleast one of the measures of value, measures of breadth, measures ofcoverage, or the technology areas associated with the organizationsand/or the intellectual-property assets. In addition, the IPstrategy-related services 128 may include determining metrics forintellectual-property documents of the customer 110, such as byutilizing the IP trend analyzer 158. The metrics may indicate trends inat least one of the number of intellectual-property assets of thecustomer 110 being filed or the number of intellectual-property assetsof the customer 110 being granted.

The intellectual-property services system 102 may also be utilized toprovide IP risk-related services 130 to the customer 110. The IPExposed-Related Services 130 may include IP liability 166, collateralprotection 168, theft of trade secrets 170, IP litigation transfer 172,source code diligence 174, and/or design-around consulting. The IPexposure-related services 130 may be related to determining, utilizingthe IP liability component 166, measures of risk of loss related tointellectual-property assets of the customer 110. The risk of loss maycorrespond to at least one of a decrease in value of anintellectual-property assets, invalidation of at least a portion of anintellectual-property asset, or theft of an intellectual-property asset.In various implementations, the IP exposure-related services 130 mayinclude determinations of measures liability with respect tointellectual-property assets of the customer 110. Theintellectual-property services system 102 may determine measures ofliability of intellectual-property assets based on at least one of anumber of litigation events of intellectual-property assets of thecustomer 110 or a number of litigation events of intellectual-propertyassets that are in a same technology classification as one or moreintellectual-property assets of the customer 110. A litigation event mayinclude filing of a request to initiate an action against anintellectual-property asset. Actions against intellectual-propertyassets may include at least one of opposition proceedings, proceedingsdecided by an administrative body, or proceedings in a judicialjurisdiction. In particular implementations, measures of liability withrespect to intellectual-property assets may correspond to a number oflitigation events related to intellectual-property assets of thecustomer 110 or intellectual-property assets of another organizationthat have taken place within a specified period of time. In someinstances, measures of liability with respect to intellectual-propertyassets may be used to determine, utilizing the collateral protectioncomponent 168, terms of insurance policies issued to protect loans madewith intellectual-property assets as collateral.

The IP exposure-related services 130 may also include determiningmeasures to reduce risk of loss with respect to intellectual-propertyassets. For example, the IP risk-related services 130 may includedetermining, utilizing the theft of trade secrets component 170, anamount of risk for the theft of trade secrets of the customer 110. Inparticular implementations, the intellectual-property services system102 may analyze security protocols or other security processesimplemented by the customer 110 to protect trade secrets and determinean amount of risk of trade secret theft based at least partly on theanalysis. The IP exposure-related services 130 may also includedetermining, utilizing the source code diligence component 174,processes and/or procedures to safeguard source code developed by thecustomer and actions for the customer 110 and processes and/orprocedures to take to protect the intellectual property rights relatedto the source code. Additionally, the IP exposure-related services 130may include determining, utilizing the design-around consultingcomponent 176, options for the customer 110 to design aroundintellectual-property assets of competitors and/or options forcompetitors of the customer 110 to design around intellectual-propertyassets of the customer 110. In particular implementations, theintellectual-property services system 102 may analyze a number ofintellectual-property assets and determine features of theintellectual-property assets that correspond to features of productsand/or services. The intellectual-property services system 102 can thenidentify features of the products and/or services that can be modifiedto avoid the features of the intellectual-property assets related to theproducts and/or services.

Further, the IP exposure-related services 130 may include determining,utilizing the IP litigation transfer component 172, strategy inintellectual property litigation actions. To illustrate, theintellectual-property services system 102 may analyze a series of eventsthat has taken place with respect to a pending litigation action inrelation to the events that took place in previous litigation actions todetermine recommendations for future decisions in the pendinglitigation. In illustrative examples, the intellectual-property servicessystem 102 may determine that motions to file in a pending litigation toincrease the probability of a favorable outcome for the customer. Theintellectual-property services system 102 may also determinerecommendations for settlement negotiations, such as amounts to offer inrelation to settlement negotiations and/or timing of settlement offers.In addition, the intellectual-property services system 102 may generaterecommendations for litigation counsel to retain in a particularlitigation action and/or generate recommendations regardingmodifications to the litigation counsel being retained.

In various implementations, the intellectual property customer services126 provided via the intellectual-property services system 102 mayinclude IP valuation services 132. The IP valuation services 132 mayinclude IP stack valuation 178, M&A sell-side and buy-side services 180,asset-backed lending 182, and/or value articulation 184. The IPvaluation services 132 may include determining, utilizing the IP stackvaluation 178, measures of value of intellectual-property assets. Inparticular implementations, the intellectual-property services system102 may determine measures of value of intellectual-property assets forthe customer or determine measures of value of intellectual-propertyassets of another organization. In some examples, theintellectual-property services system 102 may determine measures ofvalue of intellectual-property assets that may be purchased or licensedby the customer 110. The intellectual-property services system 102 mayalso determine, utilizing the M&A sell-side and buy-side services 180,measures of value of intellectual-property assets of an organizationthat may be purchased or otherwise acquired by the customer 110. Inadditional implementations, the intellectual-property services system102 may determine measures of value of intellectual-property assets ofthe customer 110 in conjunction with an acquisition of the customer 110by another organization or merger of the customer 110 with anotherorganization. Further, the intellectual-property services system 102 maydetermine, utilizing the asset-backed lending services 182, measures ofvalue of intellectual-property assets of the customer 110 in relation toone or more loans made to the customer 110 with theintellectual-property assets of the customer 110 being used ascollateral for at least a portion of the loan amount.

The intellectual-property services system 102 may determine, utilizingthe value articulation services 184, measures of value ofintellectual-property assets based on measures of breadth of theintellectual-property assets. Additionally, the intellectual-propertyservices system 102 may determine measures of value ofintellectual-property assets based on revenue of products and/orservices that correspond to the intellectual-property assets. In orderto determine the measures of breadth and/or portions of revenue of theproducts and/or services corresponding to the intellectual-propertyassets, the intellectual-property services system 102 may utilize one ormore linguistic analysis techniques and one or more machine learningtechniques.

FIG. 2 illustrates an example environment 200 to analyze a number oftypes of intellectual-property data and product/service data to provideservices related to intellectual property according to someimplementations. The environment 200 may include theintellectual-property mapping and learning system 104, the one or moredata sources 106, and the intellectual-property knowledge data store120. The intellectual-property mapping and learning system 104 may beimplemented by one or more computing devices 202. The one or morecomputing devices 202 can be included in a cloud computing architecturethat operates the one or more computing devices 202 on behalf of anintellectual-property services provider. In these scenarios, the cloudcomputing architecture may implement one or more virtual machineinstances on behalf of the intellectual-property services provider onthe one or more computing devices 202. The cloud computing architecturemay be located remotely from the intellectual-property servicesprovider. In additional implementations, the one or more computingdevices 202 can be under the direct control of the intellectual-propertyservices provider. For example, the intellectual-property servicesprovider may maintain the one or more computing devices 202 in one ormore geographic locations to perform operations related to analyzingintellectual-property data and data related to products and/or services.

The intellectual-property knowledge data store 120 may store informationthat may be utilized by the intellectual-property mapping and learningsystem 104 in providing services related to intellectual-propertyassets. In particular implementations, the intellectual-propertyknowledge data store 120 may store intellectual-property (IP) data 204.The IP data 204 may include data related to intellectual-propertyassets. The IP data 204 may be obtained via one or more publiclyaccessible data sources, one or more private data sources, orcombinations thereof. The IP data 204 may also include customer IP data206 that corresponds to data stored by the intellectual-propertyknowledge data store 120 that is related to customers obtaining servicesfrom the intellectual-property services provider. In someimplementations, the customer IP data 216 may be stored separately fromIP data of other organizations in the intellectual-property knowledgedata store 120.

In various implementations, the IP data 204 may include data related tointellectual-property assets, such as trademarks, copyrights, patents,and trade secrets. The IP data 204 may include documents that includeinformation related to various types of intellectual property. Forexample, the IP data 204 may include patent applications, publishedpatent applications, and issued or granted patents. The IP data 204 mayalso include trademark applications and submissions made in conjunctionwith the protection of copyrights. Additionally, the IP data 204 mayinclude documents that include trade secrets and documents that supportthe protection of trade secrets. To illustrate, the IP data 204 mayinclude employment agreements, employee manuals, policies, and/orprocedures of organizations that may be used to support the trade secretstatus of innovation of the organizations.

The IP data 204 may also include bibliographic information forintellectual-property documents. In illustrative examples, the IP data204 may include information particular dates related tointellectual-property documents (e.g., filing dates, issue dates,priority dates), assignees of intellectual-property documents,assignment history of intellectual-property documents, significantindividuals related to the intellectual-property documents (e.g.,inventors, examiners, etc.), third-party classifications related tointellectual-property documents, indications of priority documents forcertain intellectual-property documents, status of anintellectual-property document with an intellectual propertyjurisdiction or examining organization, combinations thereof, and thelike. In addition, the IP data 204 may include information related toprosecution history of intellectual-property documents. The prosecutionhistory may include various events that took place with respect to theexamination of intellectual-property documents. To illustrate, the IPdata 204 may include dates that documents were filed during examinationof intellectual-property documents, such as dates when responses werefiled, dates that examiners issued office actions or examinationreports, dates of allowance, dates of issuance, combinations thereof,and so forth. Further, the IP data 204 may include documents that werefiled and/or submitted during prosecution of intellectual-propertydocuments. In illustrative examples, the IP data 204 may include officeactions, office action responses, information disclosure statements,application data sheets, declarations, specimens to support use oftrademarks, appeal briefs, examiner answers to appeal briefs, replybriefs, decisions on appeal, notices of allowances, oppositiondocuments, copyright submissions, interview summary documents,combinations thereof, and the like.

The IP data 204 may also include statistics and/or metrics related toindividual examiners that examine intellectual-property assets. Toillustrate, the IP data 204 may include number of intellectual-propertyassets allowed over a period of time, average number of office actionsprovided during examination of intellectual-property assets, number ofappeals over a period of time, decisions on appeal, average length oftime to provide office actions, years of experience, number ofintellectual-property assets examined over a period of time,combinations thereof, and so forth. Further, the IP data 204 may includestatistics and/or metrics related to groups of examiners that examinerintellectual-property assets. The IP data 204 may also includestatistics and/or metrics of individual examiners with respect to thestatistics and/or metrics of a group of examiners. For example, the IPdata 204 may include number of office actions provided per allowedmatter for an individual patent examiner with respect to an averagenumber of office actions provided per allowed matter for a group ofpatent examiners that includes the individual patent examiner, such as agroup of patent examiners in a particular art unit or a particulartechnology classification.

In various implementations, the IP data 204 may include data related tolitigation proceedings and/or pseudo-litigation proceedings associatedwith intellectual-property assets. In certain implementations, the IPdata 204 may include documents filed during litigation proceedings, suchas petitions, answers, pleadings, motions, discovery requests, discoveryresponses, expert opinions, decisions by a court, jury verdicts, jurycharges, combinations thereof, and the like. In additionalimplementations, the IP data 204 may include transcripts of litigationsproceedings. For example, the IP data 204 may include transcripts ofcourt proceedings and/or transcripts of depositions. In furtherimplementations, the IP data 204 may include documents filed duringpseudo-litigation proceedings, such as inter partes review proceedingsin the United States Patent and Trademark Office or oppositionproceedings in the European Patent Office.

The intellectual-property knowledge data store 120 may also store IPvaluation data 208. The IP valuation data 208 may be used by theintellectual-property mapping and learning system 104 to determine thevalue of intellectual-property assets or portions ofintellectual-property assets. In particular implementations, the IPvaluation data 208 may include values reached during settlementnegotiations that took place during litigation proceedings orpseudo-litigation proceedings. Additionally, the IP valuation data 208may include terms of licenses obtained with respect tointellectual-property assets or portions of intellectual-propertyassets. The IP valuation data 208 may also include verdicts provided byjudges, juries, other judicial bodies, or administrative bodies thatindicate value of intellectual-property assets or portions ofintellectual-property assets. In various implementations, at least aportion of the IP valuation data 208 may include information related tocustomers of the intellectual-property services provider that is notpublicly available. In additional implementations, the IP valuation data208 may include information that may be used to determine the value ofintellectual-property assets or portions of intellectual-property assetsthat is publicly available.

In addition, the intellectual-property knowledge data store 120 maystore business data 210. The business data 210 may includeproduct/service data 212 and economic data 214. The product/service data212 may include data associated with products and/or services that areoffered for acquisition by various organizations. The product/servicedata 212 may include descriptions of products and/or services,specifications of products and/or services, product manuals, pricing ofproducts and/or services, number of sales of products and/or services,descriptions of organizations that provide various products and/orservices, combinations thereof, and the like. The product/service data212 may include customer product/service data 216 that includesinformation that is related to products and/or services offered bycustomers of the intellectual-property services provider. In someimplementations, the customer product/service data 216 may be storedseparately from product/service data of other organizations in theintellectual-property knowledge data store 120.

The economic data 214 may include information indicating financialperformance of organizations offering products and/or services foracquisition. The financial performance information may include revenueof organizations over a period of time, profit of organizations over aperiod of time, expenses of organizations over a period of time,projections of financial performance, or combinations thereof. Theeconomic data 214 may also include amount of revenue of organizationsthat corresponds to sales of one or more products and/or services. Theeconomic data 214 may include customer economic data 218 that includeseconomic data that corresponds to customers of the intellectual-propertyservices provider. In some implementations, the customer economic data218 may be stored separately from economic data of other organizationsin the intellectual-property knowledge data store 120.

Additionally, the economic data 204 may also include industry financialdata. For example, the economic data 204 may include revenue, profit,expenses, and the like for certain industries that provide goods and/orservices for acquisition, such as a retail industry, a semiconductorindustry, or transportation industry. Further, the economic data 204 mayinclude economic data of various states, counties, countries, or otherpolitical jurisdictions. To illustrate, the economic data 204 mayinclude gross domestic product data, employment data, trade data,combinations thereof, and so forth. In some instances, the economic data204 may indicate an amount of gross domestic product of a country orpolitical jurisdiction attributed to one or more industry segments.

Further, the intellectual-property knowledge data store 120 may store atleast one technology taxonomy 220. The technology taxonomy 220 mayinclude a number of classifications for products and/or services. Thetechnology taxonomy 220 may also include one or more criteria associatedwith individual classifications of the technology taxonomy 220. Forexample, to be classified according to a particular classification ofthe technology taxonomy 220, a product and/or service may correspond toat least a threshold number of criteria of a particular classification.In various implementations, the technology taxonomy 220 may indicateproducts and/or services that are associated with individualclassifications. That is, products and/or services that have previouslybeen assigned to a classification may be included in the technologytaxonomy 220.

The technology taxonomy 220 may be generated by theintellectual-property mapping and learning system 104, in someimplementations. In addition, in particular implementations, at least aportion of the technology taxonomy 220 may be generated by an additionalorganization. To illustrate, the technology taxonomy 220 may includeclassifications that are included in classification systems ofgovernmental organizations and/or classification systems of industryorganizations. In illustrative examples, at least a portion of theclassifications of the technology taxonomy 220 may correspond totechnology classifications of the United States Patent and TrademarkOffice. In other illustrative examples, at least a portion of theclassifications included in the technology taxonomy 220 may correspondto technology classifications included in the International PatentClassification (IPC), the Locarno Classification, the NiceClassification, and/or the Vienna Classification.

In various implementations, the intellectual-property knowledge datastore 120 may store intellectual property (IP) to products and/orservices mappings 222. The IP to products and/or services mappings 222may indicate intellectual-property assets or portions ofintellectual-property assets that have been mapped to a product and/orservice. In an illustrative example, the IP to products and/or servicesmappings 222 may indicate a claim of a patent document that correspondsto a feature of a mobile device, such as a microphone of the mobiledevice. In another illustrative example, the IP to products and/orservices mappings 222 may indicate a trademark that corresponds to aremote data storage service. The IP to products/services mappings 222may also indicate an organization that offers the respective productsand/or services for acquisition. Additionally, the IP to products and/orservices mappings 222 may indicate the owners of theintellectual-property assets mapped to particular products and/orservices.

The IP to products and/or services mappings 222 may include customermappings 224 that indicate mappings between products and/or services ofcustomers of an intellectual-property services provider andintellectual-property assets of customers of the intellectual-propertyservices provider. In additional implementations, the customer mappings224 may include mappings between intellectual-property assets ofcustomers of the intellectual-property services provider and productsand/or services offered by organizations that are not customers of theintellectual-property services provider. Further, the customer mappingsmay include mappings between products and/or services offered bycustomers of the intellectual-property services provider andintellectual-property assets of organizations that are not customers ofthe intellectual-property services provider.

The intellectual-property knowledge data store 120 may also storeprevious customer service data 226. The previous customer service data226 may include data that was generated by an intellectual-propertyservices provider when providing services to one or more customers. Forexample, the previous customer service data 226 may include datagenerated by the intellectual-property services provider in providingthe IP strategy-related services 128, the IP exposure-related services130, and/or the IP valuation services 132 described in relation toFIG. 1. In illustrative examples, the previous customer service data 226may include valuations of intellectual-property assets that weredetermined by the intellectual-property services provider. In additionalillustrative examples, the previous customer service data 226 mayinclude determinations of risk with respect to intellectual-propertyassets of customers of the intellectual-property services provider. Infurther illustrative examples, the previous customer service data 226may include claim charts, strategic IP analyses, and/or portfolioanalysis data generated by the intellectual-property services providerwhen providing services to customers. In certain implementations, theprevious customer service data 226 may be utilized to provide subsequentservices to customers of the intellectual-property services provider. Inthis way, the knowledge generated by the intellectual-property servicesprovider may increase and be used to more efficiently and accuratelyprovide services to customers of the intellectual-property servicesprovider.

FIG. 3 illustrates an example environment 300 to generate mappingsbetween products and intellectual-property assets using a technologytaxonomy according to some implementations. The environment 300 mayinclude the intellectual-property mapping and learning system 104 thatis implemented via one or more computing devices 202. The environment300 may also include the customer 110 of the intellectual-propertyservices provider and a group of intellectual-property assets 302 of thecustomer 110. The group of intellectual-property assets 302 may includea first IP asset 304, a second IP asset 306, a third IP asset 308, afourth IP asset 310, a fifth IP asset 312, up to an Nth IP asset 314.The IP assets 304, 306, 308, 310, 312, 314 may include various types ofintellectual property. For example, the IP assets 304, 306, 308, 310,312, 314 may include trademarks, patents, trade secrets, copyrights,know-how, or other classifications of intellectual property. Inadditional examples, at least a portion of the IP assets 304, 306, 308,310, 312, 314 may correspond to a portion of an intellectual-propertyasset, such as one or more claims of a set of claims of a patentdocument.

In various implementations, one or more of the IP assets 304, 306, 308,310, 312, 314 may correspond to a different classification ofintellectual property than at least another one of the IP assets 304,306, 308, 310, 312, 314. For example, the first IP asset 304 maycorrespond to a trademark and the second IP asset 306 may correspond toa trade secret. Additionally, in some implementations, each of the IPassets 304, 306, 308, 310, 312, 314 may correspond to a same type ofclassification of intellectual property. To illustrate, the IP assets304, 306, 308, 310, 312, 314 may each correspond to patents or patentapplications, such as at least a portion of a patent portfolio of thecustomer 110. In another illustrative example, the IP assets 304, 306,308, 310, 312, 314 may each correspond to claims of a patent or patentapplication. In an additional illustrative example, the IP assets 304,306, 308, 310, 312, 314 may each correspond to trade secrets. In afurther illustrative example, the IP assets 304, 306, 308, 310, 312, 314may each correspond to trademarks. In other illustrative examples, theIP assets 304, 306, 308, 310, 312, 314 may each correspond tocopyrights.

Additionally, the environment 300 may include the technology taxonomy220 of FIG. 2. The technology taxonomy 220 may include a number ofclassifications, such as a first classification 316, a secondclassification 318, a third classification 320, up to an Nthclassification 322. The individual classifications 316, 318, 320, 322 ofthe technology taxonomy 220 may be related to individual sets ofcriteria that characterize items associated with a particularclassification of the technology taxonomy 220. At least one ofintellectual-property assets, products, or services may be classifiedaccording to at least one classification of the technology taxonomy 220.In illustrative implementations, the intellectual-property mapping andlearning system 104 may determine features of the firstintellectual-property asset 304 and compare the features of the firstintellectual-property asset 304 to the set of criteria of the firstclassification 316. In particular implementations, theintellectual-property mapping and learning system 104 may determine ametric indicating an amount of similarity between the features of thefirst intellectual-property asset 304 and the set of criteria of thefirst classification 316.

In various implementations, the amount of similarity between thefeatures of the first IP asset 304 and the set of criteria of the firstclassification 316 may indicate a number of features of the first IPasset 304 that correspond to one or more criteria of the firstclassification 316. In illustrative implementations, theintellectual-property mapping and learning system 304 may determine theamount of similarity between a feature of the first IP asset 304 and thefirst classification 316 by comparing words of one or more features ofthe first IP asset 304 with words of the first classification 316. Theintellectual-property mapping and learning system 304 may determine thata feature of the first IP asset 304 corresponds to the firstclassification 316 based on at least a threshold number of words of afeature of the first IP asset 304 corresponding to words of the firstclassification 316. In some scenarios, the intellectual-property mappingand learning system 304 may determine that a word of a feature of thefirst IP asset 304 corresponds to a word of the first classificationwhen a spelling of the word of the first IP asset 304 is the same as aword of the first classification 316. In additional situations, theintellectual-property mapping and learning system 304 may determine thata word of a feature of the first IP asset 304 corresponds to a word ofthe first classification 316 based on the word of the first IP asset 304being a synonym of the word of the first classification 316. In furtherexamples, the intellectual-property mapping and learning system 104 maydetermine that a word of a feature of the first IP asset 304 correspondsto a word of the first classification 316 based on the word of the firstIP asset 304 being a derivative of the word of the first classification316. For example, the word of the first IP asset 304 may be a differenttense of the word of the first classification 306. In other examples,the word of the first IP asset 304 may be a plural or singular versionof the word of the first classification 316.

The intellectual-property mapping and learning system 104 may determinea first amount of similarity between a feature of the first IP asset 304and the first classification 316 based on determining that a singlefeature of the first IP asset 304 corresponds to a single criteria ofthe first classification 316. Additionally, the intellectual-propertymapping and learning system 104 may determine a second amount ofsimilarity between the first IP asset 304 and the first classification316 based on determining that two features of the firstintellectual-property asset 304 correspond to at least one criteria ofthe first classification 316. In certain implementations, theintellectual-property mapping and learning system 104 may determine thatthe first IP asset 304 corresponds to the first classification 316 basedon an amount of similarity between a feature of the first IP asset 304and the criteria of the first classification 316 being above a thresholdamount of similarity. In additional implementations, theintellectual-property mapping and learning system 104 may determine thatthe first IP asset 304 corresponds to the first classification based onan amount of similarity between a feature of the first IP asset 304 andthe first classification 316 is greater than amounts of similaritybetween the feature of the first IP asset 304 and respective sets ofcriteria of the additional classifications of the technology taxonomy220, such as the sets of criteria of the second classification 318, thethird classification 320, up to the Nth classification 322.

The intellectual-property mapping and learning system 104 may alsodetermine classifications of the technology taxonomy 220 for a number ofproducts and/or services, such as a first product 324, a second product326, and a third product 328. The intellectual-property mapping andlearning system 104 may determine classifications of the technologytaxonomy 220 for the products 324, 326, 328 themselves. In additionalimplementations, the intellectual-property mapping and learning system104 may determine classifications of the technology taxonomy 220 thatcorrespond to one or more features of the products 324, 326, 328. In anillustrative example, the intellectual-property mapping and learningsystem 104 may determine that the first product 324 corresponds to atransportation classification of the technology taxonomy 220, the secondproduct 326 corresponds to a mobile communication device classificationof the technology taxonomy 220, and the third product 328 corresponds toa printing device classification of the technology taxonomy 220. Inadditional illustrative examples, the intellectual-property mapping andlearning system 104 may determine a classification for a feature commonto the products 324, 326, 328, such as a display device included in thefirst product 324, a display device of the second product 326, and adisplay device of the third product 328. The intellectual-propertymapping and learning system 104 may also determine classifications ofadditional individual features of the products 324, 326, 328 withrespect to the technology taxonomy 220.

In particular implementations, the intellectual-property mapping andlearning system 104 may determine classifications of the technologytaxonomy 220 for the products 324, 326, 328 and/or features of theproducts 324, 326, 328 based at least partly on words describing theproducts 324, 326, 328 and/or words describing the features of theproducts 324, 326, 328 in relation to the sets of criteria of theclassifications of the technology taxonomy 220, such as the respectivesets of criteria of the classifications 316, 318, 320, 322. For example,the intellectual-property mapping and learning system 104 may determinean amount of similarity between descriptions of the products 324, 326,328 and/or features of the products 324, 326, 328 and the criteria ofthe classifications of the technology taxonomy 220.

In illustrative implementations, the amount of similarity between afeature of the first product 324 and the set of criteria of the firstclassification 316 may indicate a number of words describing the featureof the first product 324 that correspond to one or more criteria of thefirst classification 316. That is, the intellectual-property mapping andlearning system 104 may compare one or more words describing the featureof the first product 324 with words related to the first classification316 and determine a number of words of the description of the feature ofthe first product 324 that correspond to words of one or more criteriaof the first classification 316. The intellectual-property mapping andlearning system 304 may determine that a word describing a feature ofthe first product 324 corresponds to a word associated with the firstclassification 316 when a spelling of the word of the feature of thefirst product 324 is the same as a word associated with the firstclassification 316. In additional situations, the intellectual-propertymapping and learning system 104 may determine that a word describing afeature of the first product 324 corresponds to a word associated withthe first classification 316 based on the word describing the feature ofthe first product 324 being a synonym of the word associated with thefirst classification 316. In further examples, the intellectual-propertymapping and learning system 104 may determine that a word describing afeature of the first product 324 corresponds to a word associated withthe first classification 316 based on the word describing the feature ofthe first product 324 being a derivative of the word associated with thefirst classification 316. For example, the word describing the featureof the first product 324 may be a different tense of the word associatedwith the first classification 306. In other examples, the worddescribing the feature of the first product 324 may be a plural orsingular version of the word associated with the first classification316.

The intellectual-property mapping and learning system 304 may determinethat a feature of the first product 324 corresponds to the firstclassification 316 based on at least a threshold number of wordsdescribing the feature of the first product 324 corresponding to wordsassociated with the first classification 316. In some scenarios, theintellectual-property mapping and learning system 104 may determine anamount of similarity between words describing a feature of the firstproduct 324 and words associated with the first classification 316 todetermine whether or not the features of the first product 324 is to beclassified according to the first classification 316. In particularimplementations, the intellectual-property mapping and learning system104 may determine that a feature of the first product 324 corresponds tothe first classification 316 based on an amount of similarity betweenwords describing the feature of the first product 324 and words of thefirst classification 316 being above a threshold amount of similarity.In additional implementations, the intellectual-property mapping andlearning system 104 may determine that a feature of the first product324 corresponds to the first classification 316 based on an amount ofsimilarity between words describing a feature of the first product 324and words of the first classification 316 being greater than amounts ofsimilarity between the words describing the feature of the first product324 and words associated with the respective criteria of the additionalclassifications of the technology taxonomy 220, such as words associatedwith the sets of criteria of the second classification 318, the thirdclassification 320, up to the Nth classification 322.

The intellectual-property mapping and learning system 104 may alsodetermine mappings 330 between products and/or services and the group ofintellectual-property assets 302. In particular implementations, theintellectual-property mapping and learning system 104 may utilize thetechnology taxonomy 220 to determine features of the group of IP assets302 that correspond to features of one or more products and/or services.In various implementations, the intellectual-property mapping andlearning system 104 may determine mappings between features of anintellectual-property asset included in the group ofintellectual-property assets 302 and features of a product and/orservice that are classified according to a same classification of thetechnology taxonomy 220. The mappings 330 may indicate that anintellectual-property asset may cover a feature of a product. Inillustrative implementations, the mappings 330 may indicate that anintellectual-property asset may be asserted in a judicial proceedingand/or an administrative proceeding against the corresponding product.

The illustrative example of FIG. 3 includes a first mapping 332 betweenthe first product 324 and a group of intellectual-property assets thatincludes the first IP asset 304 and the third IP asset 308. The mappings330 may also include a second mapping 334 between the second product 326and another group of intellectual-property assets that includes thefirst IP asset 304, the second IP asset 306, and the fourth IP asset310. In addition, the mappings 330 may include a third mapping 336between the third product 328 and an additional group ofintellectual-property assets including the first IP asset 304 and thefifth IP asset 312.

The intellectual-property mapping and learning system 104 may determinethe mappings 332, 334, 336 by determining similarities between theproducts 324, 326, 328 and/or features of the products 324, 326, 328 andthe IP assets 304, 306, 308, 310, 312, 314 and/or features of the IPassets 304, 306, 308, 310, 312, 314. In particular implementations, theintellectual-property mapping and learning system 104 may determinemappings between features of intellectual-property assets 304, 306, 308,310-, 312, 314 and features of products 324, 326, 328 that areclassified according to a same classification of the technology taxonomy220. In various implementations, the intellectual-property mapping andlearning system 104 may determine the mapping between a feature of thefirst IP asset 304 and a feature of the first product 324 by determiningan amount of similarity between words of the feature of the first IPasset 304 and words describing the feature of the first product 324.

In an illustrative example, the intellectual-property mapping andlearning system 104 may determine an amount of similarity between anelement of a claim related to the first IP asset 304 and a feature ofthe first product 324. In another illustrative example, theintellectual-property mapping and learning system 104 may determine anamount of similarity between a trademark related to the first IP asset304 and a word or group of words used in the marketing and branding ofthe first product 324. The intellectual-property mapping and learningsystem 104 may determine the amount of similarity between the firstwords of the feature of the first IP asset 304 and second words of afeature of the first product 324 by performing a comparison between thefirst words and the second words. The amount of similarity between thefirst words and the second words may be based on a number of words thatare the same between the first words and the second words, a number ofwords that are synonyms between the first words and the second words,and/or a number of words that are derivatives between the first wordsand the second words.

In additional implementations, the intellectual-property mapping andlearning system 104 may determine mappings between intellectual-propertyassets and products and/or services based at least partly on linguisticstructures generated for the intellectual-property assets and linguisticstructures generated for the products and/or services. The linguisticstructures may indicate relationships between words of theintellectual-property assets and words describing the products. Invarious implementations, the intellectual-property mapping and learningsystem 104 may generate linguistic structures for features of theintellectual-property assets and generate linguistic structures forfeatures of the products and compare the linguistic structures of thefeatures of the intellectual-property assets and the features of theproducts.

In particular implementations, the intellectual-property mapping andlearning system 104 may determine an amount of similarity between alinguistic structure of a feature of an intellectual-property asset anda linguistic structure of a feature of a product. In certainimplementations, the intellectual-property mapping and learning system104 may compare words included in the linguistic structure of thefeature of the intellectual-property asset, such as a feature of thefirst intellectual-property asset 304, with words included in thelinguistic structure of a feature of a product, such as a feature of thefirst product 324. Additionally, the intellectual-property mapping andlearning system 104 may compare a configuration of the linguisticstructure of the feature of the first intellectual-property asset 104with a configuration of the linguistic structure of the feature of thefirst product 324. The configuration of the linguistic structure of thefeature of the first intellectual-property asset 304 may indicate firstrelationships between words related to the feature of the firstintellectual-property asset 304 and the configuration of the linguisticstructure of the feature of the first product 324 may indicaterelationships between words describing the feature of the first product324. The intellectual-property mapping and learning system 104 maygenerate a mapping between a feature of an intellectual-property assetand a feature of a product based at least partly on an amount ofsimilarity between a linguistic structure of the feature of theintellectual-property asset and a linguistic structure of a feature ofthe product being greater than a threshold amount of similarity.

FIG. 4 illustrates an example system 400 to generate valuations forintellectual-property assets according to some implementations. Thesystem 400 may include the intellectual-property mapping and learningsystem 104 and one or more computing devices 202 that may implement theintellectual-property mapping and learning system 104. The system 400may also include a first data store that stores intellectual-property(IP) valuation data 402 and a second data store that stores businessdata 404. The IP valuation data 402 and the business data 404 mayinclude information corresponding to customers of anintellectual-property service provider. The IP valuation data 402 andthe business data 404 may also include information corresponding toorganizations that are not customers of the intellectual-propertyservices provider.

The IP valuation data 402 may include information that may be used todetermine values of intellectual-property assets. In particularimplementations, the IP valuation data 402 may include verdictsindicating damages awarded during judicial proceedings related tointellectual-property assets. The IP valuation data 402 may also includeamounts for licensing intellectual-property assets. In addition, the IPvaluation data 402 may include amounts paid as part of settlementsrelated to judicial proceedings and/or administrative proceedings thattook place with regard to intellectual-property assets. The businessdata 404 may include information indicating revenue obtained byorganizations with respect to products and/or services offered by theorganizations. The business data 404 may also include other financialinformation related to organizations, such as overall revenue over aperiod of time, amount of revenue within a particular technology areaover a period of time, profit obtained over a period of time,expenditures over a period of time, combinations thereof, and so forth.In various implementations, the expenditures included in the businessdata 404 may indicate expenditures of an organization to offer one ormore products and/or services for acquisition to consumers.

The intellectual-property mapping and learning system 104 may utilize atleast one of the IP valuation data 402 or the business data 404 todetermine valuations for one or more intellectual-property assets. In anillustrative example, the intellectual-property mapping and learningsystem 104 may determine valuations for intellectual-property assetsthat corresponds to the second product 326 of FIG. 3. In particular, theintellectual-property mapping and learning system 104 may determinevaluations for intellectual-property assets that are mapped to featuresof the second product 326, such as the first IP asset 304, the second IPasset 306, and the fourth IP asset 310. For example, theintellectual-property mapping and learning system 104 may determine afirst valuation 406 for the first IP asset 304, a second valuation 408for the second IP asset 306, and a third valuation 410 for the fourth IPasset 310. The valuations 406, 408, 410 may indicate a monetary valuethat the organization(s) that own the rights to the respective IP assets304, 306, 310 may obtain from one or more additional organizations inexchange for rights to the IP assets 304, 306, 310. In variousimplementations, the valuations 406, 408, 410 may indicate one or moremonetary values that the organization(s) that own the rights to therespective IP assets 304, 306, 310 may obtain in one or more licensingtransactions that involve the IP assets 304, 306, 310. In additionalimplementations, the valuations 406, 408, 410 may indicate one or moremonetary values that the organization(s) that own the rights to therespective IP assets 304, 306, 310 may obtain with respect to a sale ofthe IP assets 304, 306, 310. Further, the valuations 406, 408, 410 mayindicate one or more monetary values of the respective IP assets 304,306, 310 during a merger or an acquisition of an organization that ownsthe rights to the IP assets 304, 306, 310 with respect to an additionalorganization. In still other implementations, the valuations 406, 408,410 may indicate one or more monetary values of the respective IP assets304, 306, 310 as collateral for a loan to an organization that owns therights to the IP assets 304, 306, 310.

The intellectual-property mapping and learning system 104 may determinevaluations of intellectual-property assets by determining an amount ofrevenue of a product and/or service to attribute to theintellectual-property assets. In particular implementations, theintellectual-property mapping and learning system 104 may determine anamount of revenue of a product and/or service to attribute to anintellectual-property asset based at least partly on a breadth of theintellectual-property asset covering the product and/or service withrespect to other intellectual-property assets included in a sameclassification of a framework of classifications, such as the technologytaxonomy 220 of FIG. 2. In additional implementations, theintellectual-property mapping and learning system 104 may determine anamount of revenue of a product and/or service to attribute to anintellectual-property asset based at least partly on a breadth of anintellectual-property asset covering the product and/or service withrespect to other intellectual-property assets covering the productand/or service.

Breadth of an intellectual-property asset may be determined based onword count of the intellectual-property asset and/or commonality ofwords of the intellectual-property asset. In particular implementations,the number of unique words and the frequency with which those wordsappear in other intellectual-property assets may be utilized todetermine a breadth value for a given intellectual-property asset. Forexample, for a given intellectual-property asset, the word count of theintellectual-property asset is compared to the word count of otherintellectual-property assets, such as a number of additionalintellectual-property assets included in a same classification as thegiven intellectual-property asset or a number of additionalintellectual-property assets covering a same product and/or service asthe given intellectual-property asset. Additionally, a commonness scoremay be determined for a given intellectual-property asset based on thecommonality of words in the intellectual-property asset as compared tothe commonality of words in other intellectual-property assets.

In situations where a given intellectual-property asset is a patentclaim, the breadth value of the claim may represent an estimated scopeof an intellectual property right relative to other patent claims, suchas other patent claims that cover a same product and/or service as thegiven patent claim or other patent claims that are classified accordingto a same classification as the given patent claim. In particularimplementations, the breadth value of a patent claim may be based atleast partly on a type of preamble included in the patent claim. Forexample, a patent claim including a preamble having a closed transitionphrase may have a breadth value that is less than a patent claimincluding a preamble having an open transition phrase. Additionally,patent claims that include certain words, such as an absolute word,exemplary word, or relative word, may have lower breadth values thanpatent claims that do not include these types of words.

Word count may include the number of words of an intellectual-propertyasset or a portion of an intellectual-property asset. In variousimplementations, a word count may be determined after duplicate wordsare removed from an initial list of words included in theintellectual-property asset. In this way, the word count may be a countof unique words of an intellectual-property asset. Additionally, a wordcount may include a number of words of the intellectual-property assetafter the removal of stop words. Stop words may include the most commonwords in a language. To illustrate, stop words may include shortfunction words such as “the” “is,” “at,” “which,” and “on,” as well asothers. The intellectual-property mapping and learning system 104 mayhave access to one or more lists of stop words for one or morelanguages. Further, a word count may be determined before or afterconverting acronyms and abbreviations into their full wordrepresentations. The word count may also include or exclude words in thepreamble. In some implementations, a number of different word counts maybe used to determine breadth of an intellectual-property asset, such asa first word count that includes a number of unique words and a secondword count that excludes stop words.

Commonality of words may correspond to the frequency that a given wordis found within a corpus of documents or within a group ofintellectual-property assets. Words that have a higher commonality, thatis words that are more common words within a corpus of words, maycorrespond to greater breadth while the presence of infrequently usedwords within a corpus of words may indicate reduced breadth. In thecontext of patent claims, words that are often found in the technicalfield are generally considered broader, or less limiting, than uncommonwords.

In illustrative implementations, the intellectual-property mapping andlearning system 104 may determine a breadth value of the firstintellectual-property asset 304 relative to breadth values of otherintellectual-property assets included in a same classification of thetechnology taxonomy 220 as the first intellectual-property asset 304.The intellectual-property mapping and learning system 104 may utilizethe relative breadth score of the first intellectual-property asset 304to determine a portion of the revenue of the second product 326 toattribute to the first intellectual-property asset 304. Theintellectual-property mapping and learning system 104 may also determinean additional relative breadth score for the first intellectual-propertyasset 304 by determining an additional breadth value for the firstintellectual-property asset 304 relative to breadth values of otherintellectual-property assets that cover the second product 326, such asthe second intellectual-property asset 306 and the fourthintellectual-property asset 310. In a particular illustrative example,the intellectual-property mapping and learning system 104 may determinethat the portion of revenue of the second product 326 to attribute tothe first intellectual-property asset 304 is 0.00625%.

In additional implementations, the intellectual-property mapping andlearning system 104 may determine valuations for intellectual-propertyassets based on licensing information, settlement information, damagesawards, or combinations thereof. For example, the intellectual-propertymapping and learning system 104 may analyze the IP valuation data 402 toidentify features of products and/or services that have been the subjectof licensing deals, settlements, and/or damages awards that correspondto features of at least one intellectual-property asset that covers aproduct and/or service, such as the intellectual-property assets 304,306, 310 that cover the second product 326. The intellectual-propertymapping and learning system 104 may then determine valuations for one ormore intellectual-property assets based on the monetary values ofsettlements, licensing deals, and/or damages awards regarding featuresof particular products and/or services that may correspond to theintellectual-property assets. In an illustrative example, theintellectual-property mapping and learning system 104 may identify aclaim of the first intellectual-property asset 304 that includes atleast one feature that has at least a threshold similarity to a featureof a product that was the subject of a damages award in a judicialproceeding. The intellectual-property mapping and learning system 104may then determine the first valuation 406 based on the amount ofsimilarity between the feature of the claim of the first IP asset 304and the feature of the product that was the subject of the damagesaward.

In additional implementations, the intellectual-property mapping andlearning system 104 may determine damages awards, licensing deals,and/or settlements related to a classification of products and/orservices that corresponds to a classification of the firstintellectual-property asset. The intellectual-property mapping andlearning system 104 may then analyze the damages awards, licensingdeals, and/or settlements of the products and/or services in the sameclassification as the first IP asset 304 to determine the firstvaluation 406. In particular implementations, the intellectual-propertymapping and learning system 104 may determine an average amount ofmonetary value for settlements, damages awards, and/or licensing dealsin a technology classification of the first IP asset 304 and determinethe first valuation 406 based on the average amount of monetary value.Further, the intellectual-property mapping and learning system 104 maydetermine a similarity between at least one feature of the first IPasset 304 and features that were the subject of damages awards,settlements, and/or licensing deals in a same classification as thefirst IP asset 304. The intellectual-property mapping and learningsystem 104 may then determine a percentage or proportion of the damagesawards, settlements, and/or licensing deals to allocate to the at leastone feature of the first IP asset 304 based on the amount of similarity.

FIG. 5 illustrates an example system 500 to modify mappings betweenintellectual property and taxonomy classifications and mappings betweenintellectual property and products/services according to someimplementations. The system 500 may include the intellectual-propertymapping and learning system 104 that is implemented by one or morecomputing devices 202. The system 500 may also include a first computingdevice 502 that is operated by a first user 504 and a second computingdevice 506 that is operated by a second user 508. In someimplementations, at least one of the first user 504 or the second user508 may be representatives of an intellectual-property servicesprovider. In additional implementations, at least one of the first user504 or the second user 508 may not be representatives of theintellectual-property services provider. For example, at least one ofthe first user 504 or the second user 508 may be a representative ofanother organization or part of a crowdsourcing group. In variousimplementations, the first user 504 and the second user 508 may, via thefirst computing device 502 and the second computing device 506,respectively, provide input regarding mappings betweenintellectual-property assets and classifications of a technologyclassification system and/or provide input regarding mappings betweenintellectual-property assets and products and/or services.

In particular implementations, the intellectual-property mapping andlearning system 104 may determine an IP asset to classification mapping510. The IP asset to classification mapping may indicate that the IPasset has been classified according to a particular classification of aframework of classifications, such as the technology taxonomy 220 ofFIG. 2. In an illustrative example, the IP asset to classificationmapping 510 may indicate that a claim of a patent is classifiedaccording to a classification related to mobile device batteries. Inanother illustrative example, the IP asset to classification mapping 510may indicate that a trademark is classified according to aclassification related to an online gaming platform. Theintellectual-property mapping and learning system 104 may send the IPasset to classification mapping 510 to the first computing device 502 inconjunction with a request for input regarding the IP asset toclassification mapping 510. The request for input may be directed to aninquiry whether the classification of the IP asset is correct or not. Invarious implementations when the IP asset to classification mapping 510is not correct, the request for input may ask for a differentclassification to assign to the IP asset.

In certain implementations, the intellectual-property mapping andlearning system 104 may generate one or more user interfaces that may bedisplayed by the first computing device 502 and may include at least oneuser interface element to capture input from the first user 504regarding the IP asset to classification mapping 510. For example, theone or more user interfaces may include at least one user interfaceelement to capture input indicating that the IP asset to classificationmapping 510 is to be modified, at least one user interface element tocapture input indicating that the IP asset to classification mapping 510is not to be modified, at least one user interface element to captureinput indicating a different classification for the IP asset, orcombinations thereof. The first user 504 may provide IP asset toclassification mapping feedback 512 to the intellectual-property mappingand learning system 104 via the one or more user interfaces.

The intellectual-property mapping and learning system 104 may analyzethe IP asset to classification mapping feedback 512 to determine whetherthe IP asset to classification mapping 510 is to be modified. Toillustrate, the intellectual-property mapping and learning system 104may analyze the IP asset to classification mapping feedback 512 todetermine whether the IP asset to classification mapping feedback 512indicates that the IP asset to classification mapping 510 is correct orwhether the IP asset is to be classified according to a differentclassification. The intellectual-property mapping and learning system104 may utilize the IP asset to classification mapping feedback 512 tomodify a framework of classifications. For example, theintellectual-property mapping and learning system 104 may modify one ormore criteria of a classification of the framework of classificationsbased on the IP asset to classification mapping feedback 512. In anillustrative example, the intellectual-property mapping and learningsystem 104 may add one or more criteria or remove one or more criteriafrom a classification based on the IP asset to classification mappingfeedback 512 indicating that the IP asset to classification mapping 510is to be modified.

In additional illustrative examples, the intellectual-property mappingand learning system 104 may modify a model that determinesclassifications of IP assets based on the IP asset to classificationmapping feedback 512. The model may include a number of factors andrespective weightings of factors that may be used to determineclassifications of IP assets. In particular implementations, the modelmay be generated using one or more machine learning techniques. Invarious implementations, the intellectual-property mapping and learningsystem 104 may modify a model utilized to determine classifications ofIP assets by removing one or more factors included in the model, addingone or more factors to the model, modifying weightings of one or morefactors included in the model, or combinations thereof.

In the illustrative example of FIG. 5, the intellectual-property mappingand learning system 104 may determine a modified IP asset toclassification mapping 514. The modified IP asset to classificationmapping 514 may indicate that the IP asset is associated with adifferent classification than the classification for the IP asset in theIP asset to classification mapping 510. The intellectual-propertymapping and learning system 104 may determine a different classificationfor the IP asset based on the IP asset to classification mappingfeedback 512. For example, in situations where the IP asset toclassification mapping feedback 512 indicates that the classification ofthe IP asset is to be modified to a particular, differentclassification, the intellectual-property mapping and learning system104 may change the classification of the IP asset to the classificationindicated in the IP asset to classification mapping feedback 512. Inadditional implementations, the intellectual-property mapping andlearning system 104 may analyze the input included in the IP asset toclassification mapping feedback 512 to modify a model that determinesclassifications of IP assets and then implement the modified model withrespect to the IP asset. The modified model may then generate themodified IP asset to classification mapping 514.

Additionally, the intellectual-property mapping and learning system 104may determine an IP asset to product/service mapping 516. The IP assetto product/service mapping 516 may indicate that at least a portion ofthe IP asset covers at least a portion of a product and/or service. Forexample, the IP asset to product/service mapping 516 may indicate that aclaim of a patent covers a user interface feature of an audioapplication executed by a mobile communication device. In anotherexample, the IP asset to product/service mapping 516 may indicate that atrade secret corresponds to a process of manufacturing a food product.The intellectual-property mapping and learning system 104 may send theIP asset to product/service mapping 516 to the second computing device506 to request input from the second user 508 regarding the IP asset toproduct/service mapping 516. The request for input may be directed to aninquiry whether the mapping between the IP asset and the product/serviceis correct or not. In various implementations when the IP asset toproduct/service mapping 516 is not correct, the request for input mayask for a different product/service to assign to the IP asset.

The intellectual-property mapping and learning system 104 may generateone or more user interfaces that may be displayed by the secondcomputing device 506 and may include at least one user interface elementto capture input from the second user 508 regarding the IP asset toproduct/service mapping 516. For example, the one or more userinterfaces may include at least one user interface element to captureinput indicating that the IP asset to product/service mapping 516 is tobe modified, at least one user interface element to capture inputindicating that the IP asset to product/service mapping 516 is not to bemodified, at least one user interface element to capture inputindicating a different product/service that corresponds to the IP asset,or combinations thereof. The second user 508 may provide IP asset toproduct/service mapping feedback 518 to the intellectual-propertymapping and learning system 104 via the one or more user interfaces.

The intellectual-property mapping and learning system 104 may analyzethe IP asset to product/service mapping feedback 518 to determinewhether the IP asset to product/service mapping 516 is to be modified.To illustrate, the intellectual-property mapping and learning system 104may analyze the IP asset to product/service mapping feedback 518 todetermine whether the IP asset to product/service mapping feedback 518indicates that the IP asset to product/service mapping 516 is correct orwhether the IP asset is to be associated with another product and/orservice. The intellectual-property mapping and learning system 104 mayutilize the IP asset to product/service mapping feedback 518 to modify aframework of classifications. For example, the intellectual-propertymapping and learning system 104 may modify one or more criteria of aclassification of the framework of classifications based on the IP assetto product/service mapping feedback 518. In an illustrative example, theintellectual-property mapping and learning system 104 may add one ormore criteria or remove one or more criteria from a classification of aframework of classifications based on the IP asset to product/servicemapping feedback 518 indicating that the IP asset to product/servicemapping 516 is to be modified.

Further, the intellectual-property mapping and learning system 104 maymodify a model that determines products and/or services that correspondto IP assets based on the IP asset to product/service mapping feedback518. The model may include a number of factors and respective weightingsof factors that may be used to determine products and/or services thatare covered by IP assets. The model may be generated using one or moremachine learning techniques. In particular implementations, theintellectual-property mapping and learning system 104 may modify a modelutilized to determine products and/or services covered by IP assets byremoving one or more factors included in the model, adding one or morefactors to the model, modifying weightings of one or more factorsincluded in the model, or combinations thereof.

In the illustrative example of FIG. 5, the intellectual-property mappingand learning system 104 may determine a modified IP asset toproduct/service mapping 520. The modified IP asset to product/servicemapping 520 may indicate that the IP asset is associated with adifferent product and/or service than the product and/or serviceassociated with the IP asset in the IP asset to product/service mapping516. The intellectual-property mapping and learning system 104 maydetermine a different product and/or service covered by the IP assetbased on the IP asset to product/service mapping feedback 518. Forexample, in situations where the IP asset to product/service mappingfeedback 518 indicates that the product and/or service associated withthe IP asset is to be modified to be associated with a different productand/or service, the intellectual-property mapping and learning system104 may change the product and/or service associated with the IP assetto the product and/or service indicated in the IP asset toproduct/service mapping feedback 518. In additional implementations, theintellectual-property mapping and learning system 104 may analyze theinput included in the IP asset to product/service mapping feedback 518to modify a model that determines products and/or services covered by IPassets and then implement the modified model with respect to the IPasset. The modified model may then generate the modified IP asset toproduct/service mapping 520.

FIG. 6 illustrates an example architecture 600 to provide intellectualproperty related services to customers using mappings betweenintellectual property and products/services in relation to aclassification system according to some implementations. Thearchitecture 600 may include an intellectual-property services provider602. The intellectual-property services provider 602 may provideservices to customers, such as the customer 110, that are related tointellectual-property assets. The intellectual-property assets may beassociated with the customers of the intellectual-property servicesprovider 602, in some scenarios. For example, customers of theintellectual-property services provider 602 may request that theintellectual-property services provider 602 provide one or more serviceswith regard to intellectual-property assets for which the customers ofthe intellectual-property services provider 602 hold the ownershiprights. In additional examples, the customers of theintellectual-property services provider 602 may request that theintellectual-property services provider 602 provide services with regardto intellectual-property assets with ownership rights held byorganizations that are not customers of the intellectual-propertyservices provider 602.

At least a portion of the operations performed by theintellectual-property services provider 602 may be performed by one ormore computing devices 604. The one or more computing devices 604 may beany suitable type of computing device, e.g., portable, semi-portable,semi-stationary, or stationary. Some examples of the one or morecomputing devices 604 may include tablet computing devices; smart phonesand mobile communication devices; laptops, netbooks and other portablecomputers or semi-portable computers; desktop computing devices,terminal computing devices and other semi-stationary or stationarycomputing devices; dedicated register devices; wearable computingdevices, or other body-mounted computing devices; augmented realitydevices; or other computing devices capable of sending communicationsand performing the functions according to the techniques describedherein.

The one or more computing devices 604 may include one or more servers orother types of computing devices that can be embodied in any number ofways. For example, in the example of a server, the modules, otherfunctional components, and data may be implemented on a single server, acluster of servers, a server farm or data center, a cloud-hostedcomputing service, a cloud-hosted storage service, and so forth,although other computer architectures can additionally or alternativelybe used.

Further, while the figures illustrate the components and data of the oneor more computing devices 604 as being present in a single location,these components and data can alternatively be distributed acrossdifferent computing devices and different locations in any manner.Consequently, the functions performed by the one or more computingdevices 604 may be implemented by one or more server computing devices,with the various functionality described above distributed in variousways across the different computing devices. Multiple computing devices604 may be located together or separately, and organized, for example,as virtual servers, server banks and/or server farms. The describedfunctionality may be provided by the servers of an intellectual-propertyservices provider, or may be provided by the servers and/or services ofmultiple different organizations.

In the illustrated example, the one or more computing devices 604 mayinclude one or more processors 606, one or more computer-readable media608, one or more communication interfaces 610, and one or moreinput/output devices 612. Each processor 606 may be a single processingunit or a number of processing units, and may include single or multiplecomputing units or multiple processing cores. The processor(s) 606 maybe implemented as one or more microprocessors, microcomputers,microcontrollers, digital signal processors, central processing units,state machines, logic circuitries, and/or any devices that manipulatesignals based on operational instructions. For example, the processor(s)606 may be one or more hardware processors and/or logic circuits of anysuitable type specifically programmed or configured to execute thealgorithms and processes described herein. The processor(s) 606 may beconfigured to fetch and execute computer-readable instructions stored inthe computer-readable media 608, which can program the processor(s) 606to perform the functions described herein.

The computer-readable media 608 may include volatile and nonvolatilememory and/or removable and non-removable media implemented in any typeof technology for storage of information, such as computer-readableinstructions, data structures, program modules, or other data. Suchcomputer-readable media 608 may include, but is not limited to, RAM,ROM, EEPROM, flash memory or other memory technology, optical storage,solid state storage, magnetic tape, magnetic disk storage, RAID storagesystems, storage arrays, network attached storage, storage areanetworks, cloud storage, or any other medium that may be used to storethe desired information and that may be accessed by a computing device.Depending on the configuration of the one or more computing devices 604,the computer-readable media 608 may be a type of computer-readablestorage media and/or may be a tangible non-transitory media to theextent that when mentioned, non-transitory computer-readable mediaexclude media such as energy, carrier signals, electromagnetic waves,and signals per se.

The computer-readable media 608 may be used to store any number offunctional components that are executable by the processor(s) 606. Inmany implementations, these functional components comprise instructionsor programs that are executable by the processor(s) 606 and that, whenexecuted, specifically configure the one or more processors 606 toperform the actions attributed above to the intellectual-propertyservices provider 602. Functional components stored in thecomputer-readable media 608 may include the intellectual-propertyservices system 104, the data acquisition system 118, the languageanalysis system 122, the IP knowledge model development system 124,intellectual property (IP) valuation tools 614, IP strategy tools 616,and IP risk tools 616. The computer-readable media 608 may also storethe data of the intellectual-property knowledge data store 120.

In at least one example, the computer-readable media 608 may include ormaintain other functional components and data, such as other modules anddata, which may include programs, drivers, one or more operatingsystems, etc., and the data used or generated by the functionalcomponents. Further, the one or more computing devices 604 may includemany other logical, programmatic and physical components, of which thosedescribed above are merely examples that are related to the discussionherein.

The communication interface(s) 610 may include one or more interfacesand hardware components for enabling communication with various otherdevices, such as over one or more network(s). For example, communicationinterface(s) 610 may enable communication through one or more of theInternet, cable networks, cellular networks, wireless networks (e.g.,Wi-Fi) and wired networks, as well as close-range communications such asBluetooth®, Bluetooth® low energy, and the like, as additionallyenumerated elsewhere herein.

The one or more computing devices 604 may further be equipped withvarious input/output (I/O) devices 612. The I/O devices 612 can includespeakers, a microphone, a camera, a display (e.g., a liquid crystaldisplay, a plasma display, a light emitting diode display, an OLED(organic light-emitting diode) display, an electronic paper display, orany other suitable type of display able to present digital contentthereon), and various user controls (e.g., buttons, a joystick, akeyboard, a keypad, etc.), a haptic output device, and so forth.Further, in particular implementations, the one or more computingdevices 604 may include one or more sensors, such as an accelerometer,gyroscope, compass, proximity sensor, camera, microphone, and/or aswitch, a GPS sensor, etc.

In particular implementations, the intellectual-property servicesprovider 602 may generate various mappings 620 that may be used toprovide intellectual property related services to customer of theintellectual-property services provider 602 For example, the mappings620 may include one or more intellectual-property asset toclassification mappings 622. The individual intellectual-propertyclassification mappings 622 may indicate a relationship between anintellectual-property asset and a classification of a classificationframework, such as a classification of the technology taxonomy 220.Additionally, the mappings 620 may include one or moreintellectual-property asset to product/service mappings 624. Theindividual intellectual-property asset to product/service mappings 624may indicate a relationship between an intellectual-property asset and aproduct and/or service. Further, the mappings 620 may include one ormore product/service to economic data mappings 626. The individualproduct/service to economic data mappings 626 may indicate specificeconomic data that is related to at least one product or service. Toillustrate, a product/service to economic data mapping 626 may indicaterevenue of a product and/or service.

The intellectual-property services provider 602 may receive requests forIP-related services and the intellectual-property services system 104may utilize the mappings, data stored by the intellectual-propertyknowledge data store, and/or additional information, such as one or moreclassification frameworks, to provide the services associated with therequest. For example, the intellectual-property services provider 602may receive requests from customers to obtain services related tointellectual property valuation, intellectual property strategy, andintellectual property risk. In particular implementations, theintellectual-property services provider 602 may utilize the IP valuationtools 614 to provide intellectual property valuation services tocustomers. The IP valuation tools 614 may include at least one of one ormore user interfaces, one or more scripts, or one or more applicationsthat may be used to analyze data related to intellectual-property assetsand provide information corresponding to the values ofintellectual-property assets of customers of the intellectual-propertyservices provider 602. Additionally, the intellectual-property servicesprovider 602 may utilize IP strategy tools 616 to provide IP strategyservices to customers. The IP strategy tools 616 may include at leastone of one or more user interfaces, one or more scripts, or one or moreapplications that may be used to analyze data related tointellectual-property assets and provide strategy related information tothe customers of the intellectual-property services provider 602.Further, the intellectual-property services provider 602 may utilize theIP exposure tools 618 to provide IP risk services to customers. The IPexposure tools 618 may include at least one of one or more userinterfaces, one or more scripts, or one or more applications that may beused to analyze data related to intellectual-property assets and providerisk related information to the customers of the intellectual-propertyservices provider 602.

In an illustrative implementation, the customer 110 may send a requestfor IP-related services 628 to the intellectual-property servicesprovider 602. The request for IP-related services 628 may be sentelectronically to the intellectual-property services provider 602. Forexample, the customer 110 may send a communication, such as an email ormessage, to the intellectual-property services provider 602 thatincludes the request for IP-related services 628. In additionalexamples, the customer 110 may access one or more user interfacesprovided by the intellectual-property services provider 602 to generatethe request for IP-related services 628. The intellectual-propertyservices provider 602 may communicate one or more aspects of the requestfor IP-related services 628 to an additional computing device 630 thatis operated by a user 632. The user 632 may be a representative of theintellectual-property services provider 602. In particularimplementations, the request for IP-related services 628 may include anumber of aspects, such as requests for one or more IP valuationservices, for one or more IP strategy services, and/or one or more IPrisk services. Individual aspects of the request may be provided to asingle representative of the intellectual-property service provider 602or to a number of representatives of the intellectual-property servicesprovider 602. In particular illustrative implementations, the requestfor IP-related services 628 may include a first request for valuationsof a portfolio of intellectual-property assets, a second request for apatent landscape analysis related to an electronic device manufacturedby the customer 110, a third request for a risk assessment related toinvalidation of a number of intellectual-property assets of the customer110, and a fourth request for a trade secret theft assessment related totrade secrets of the customer 110. In this scenario, theintellectual-property services provider 602 may, in some instances,assign the user 632 to provide services related to the first request,the second request, the third request, and the fourth request. Inadditional instances, the intellectual-property services provider 602may assign the user 632 to provide services related to one of the firstrequest, the second request, the third request, or the fourth request,and assign the tasks related to providing services associated with theremaining requests to other representatives of the intellectual-propertyservices provider 602.

In a situation where the user 632 is assigned to perform servicesrelated to the valuation of intellectual-property assets, the user 632may operate the additional computing device 630 to access the IPvaluation tools 614. In various implementations, theintellectual-property services provider 602 may obtain identifiers ofthe intellectual-property assets for which the valuations are beingdetermined from the additional computing device 630. The identifiers mayinclude identifiers provided by intellectual property jurisdictions(e.g., EPO, USPTO, JPO, etc.), such as application numbers, registrationnumbers, patent numbers, publication numbers, or combinations thereof.The identifiers may also include titles of intellectual-property assets.Further, the identifiers may be alphanumeric strings generated by theintellectual-property services provider 602 that correspond toindividual intellectual-property assets. In addition, theintellectual-property services provider 602 may obtain a situation ortype of valuation to be determined. For example, theintellectual-property services provider 602 may receive information fromthe additional computing device 630 indicating that at least one of avaluation is to be determined for the sale of the intellectual-propertyassets of the customer 110, a valuation is to be determined for thelicensing of the intellectual-property assets of the customer 110, or avaluation is to be determined for the intellectual-property assets ofthe customer 110 to be used as collateral for a loan.

After obtaining input from the additional computing device 630 via theIP valuation tools 614, the intellectual-property services system 104may access the mappings 620, data stored by the intellectual-propertyknowledge data store 120, models generated by the intellectual-propertyservices provider 602, machine learning algorithms, or combinationsthereof, to provide intellectual property customer services 634associated with the valuation of intellectual-property assets requestedby the customer 110. Depending on the type of valuation being performedand the amount of information that the intellectual-property servicesprovider 602 has already obtained with respect to theintellectual-property assets of the customer 110 for which valuationsare being performed, the intellectual-property services system 104 mayaccess one or more of the intellectual-property asset to classificationmappings, 622, the intellectual-property asset to product/servicemappings 624, or the product/service to economic data mappings 626 todetermine valuations for the intellectual-property assets of thecustomer 110 that are the subject of the request for IP-related services628.

In additional situations where the user 632 is assigned to performstrategy related services for intellectual-property assets of thecustomer 110, the user 632 may operate the additional computing device630 to access the IP strategy tools 616. In these situations, theintellectual-property services system 104 may obtain identifiers ofintellectual-property assets from the additional computing device, aswell as indications of the type of strategy related services to provide.The intellectual-property services system 104 may then access themappings 620, data stored by the intellectual-property knowledge datastore 120, models generated by the intellectual-property servicesprovider 602, machine learning algorithms, or combinations thereof, toprovide intellectual-property customer services 634 to the customer 110related to the IP strategy services requested by the customer 110.

In further scenarios where the user 632 is assigned to perform riskrelated services for intellectual-property assets of the customer 110,the user 632 may operate the additional computing device 630 to accessthe IP risk tools 618. In these situations, the intellectual-propertyservices system 104 may obtain identifiers of intellectual-propertyassets from the additional computing device, as well as indications ofthe type of risk related services to provide. The intellectual-propertyservices system 104 may then access the mappings 620, data stored by theintellectual-property knowledge data store 120, models generated by theintellectual-property services provider 602, machine learningalgorithms, or combinations thereof, to provide intellectual-propertycustomer services 634 to the customer 110 related to the IP riskservices requested by the customer 110.

In an illustrative implementation, the intellectual-property servicesprovider 602 may receive the request for IP-related services 628 fromthe customer 110, and the request for IP-related services 628 mayinclude a request for valuation of an intellectual-property asset 636 ofthe customer 110. The intellectual-property services provider 602 mayprovide the request for valuation of the intellectual-property asset 636to the additional computing device 630. In response to the request forvaluation services, the user 632 may operate the additional computingdevice 630 to access the IP valuation tools 614. The IP valuation tools614 may generate one or more user interfaces that include one or moreuser interface elements to capture information that may be used by theintellectual-property services provider 602 to determine a valuation forthe IP asset 636. In various implementations, the IP valuation tools 614may include a user interface element to capture an identifier of the IPasset 636 and a type of valuation to be determined. In a particularillustrative example, the IP asset 636 may be a US patent and theadditional computing device 630 may obtain an identifier of the IP asset636, such as a patent number of the IP asset 636, and input indicatingthat the type of valuation corresponds to a sale of the IP asset 636.

Based on the input obtained from the additional computing device 630,the intellectual-property services system 104 may determine whether themappings 620 include one or more mappings related to the IP asset 636.For example, the intellectual-property services system 104 may havepreviously determined a classification related to the IP asset 636 andgenerated an intellectual-property asset to classification mapping 622for the IP asset 636. In another example, the intellectual-propertyservices system 104 may have previously determined a product and/orservice that corresponds to the IP asset 636 and generated anintellectual-property asset to product/service mapping 624 for the IPasset 636. In additional examples, the intellectual-property servicessystem 104 may have previously determined economic data that correspondsto the IP asset 636 and generated a product/service to economic datamapping 626. In these situations, one or more of the mappings 620related to the IP asset 636 may be stored by the intellectual-propertyknowledge data store 120 and the intellectual-property services system104 may utilize the identifier of the IP asset 636 to retrieve themappings 620 that correspond to the IP asset 636. In situations wherethe mappings 620 do not include one or more mappings used to determinethe valuation of the IP asset 636, the intellectual-property servicessystem 104 may generate at least one of an intellectual-property assetto classification mapping 622 for the IP asset 636, anintellectual-property asset to product/service mapping 624 for the IPasset 636, or a product/service to economic data mapping 626 for the IPasset 636.

Continuing with the illustrative example from above, theintellectual-property services system 104 may determine anintellectual-property asset to classification mapping 622 for the IPasset 636 to determine a classification of the IP asset 636. Theintellectual-property services system 104 may then identify additionalintellectual-property assets having the same classification as the IPasset 636. The intellectual-property services system 104 may determinethe breadth of the intellectual-property asset 636 with respect to thebreadth of the other intellectual-property assets included in the sameclassification of the IP asset 636. The breadth of the IP asset 636relative to the breadth of additional IP assets in the sameclassification as the IP asset 636 may be used to determine thevaluation of the IP asset 636. In various implementations, theintellectual-property services system 104 may also obtain licensingdata, damages awards, and/or settlement data for additionalintellectual-property assets included in the same classification as theIP asset 636 and utilize the data to determine a valuation for the IPasset 636.

Additionally, the intellectual-property services system 104 maydetermine an intellectual-property asset to product/service mapping 624for the IP asset 636 that indicates a product and/or service thatcorresponds to the IP asset 636. In some situations, theintellectual-property services system 104 may identify multipleintellectual-property asset to product/service mappings 624 related tothe IP asset 636. In particular implementations, revenue related to oneor more of the products and/or services corresponding to the IP asset636 may be used to determine the valuation of the IP asset 636. Further,the intellectual-property services system 104 may determine aproduct/service to economic data mapping 626 for the IP asset 636. Theproduct/service to economic data mapping 626 for the IP asset 636 mayindicate the financial data associated with the one or more productsand/or services corresponding to the IP asset 636 and may be used by theintellectual-property services system 104 to determine a valuation forthe IP asset 636 in response to the request received from the additionalcomputing device 630

In particular implementations, the intellectual-property services system104 may generate one or more user interfaces that include one or morevaluations for the IP asset 636 and make the one or more user interfacesaccessible to the additional computing device 630. In certainimplementations, the intellectual-property services system 104 mayprovide a notification to the additional computing device 630, such asan email, message, and the like, to indicate that the one or morevaluations for the IP asset 636 have been determined.

In addition, the intellectual-property services system 104 may provideaccess to the mappings 620 related to the IP asset 636. In thesesituations, the user 632 may utilize additional computing device 630 toprovide input regarding the mappings 620 to use in determining one ormore valuations for the IP asset 636. In an illustrative example, theintellectual-property services system 104 may provide a firstintellectual-property asset to classification mapping indicating thatthe IP asset 636 is associated with a first classification and a secondintellectual-property asset to classification mapping indicating thatthe IP asset 626 is associated with a second classification. Theadditional computing device 630 may send input to theintellectual-property services provider 602 indicating selection of thefirst intellectual-property asset to classification mapping or thesecond intellectual-property asset to classification mapping. Theintellectual-property services system 604 may also provide multipleintellectual-property asset to product/service mappings 624 related tothe IP asset 636 to the additional computing device 630 and obtain inputfrom the additional computing device 630 indicating at least oneintellectual-property asset to product/service mapping 624 to utilize todetermine a valuation for the IP asset 636. Further, theintellectual-property services system 104 may provide multipleproduct/service to economic data mappings 626 corresponding to one ormore products and/or services related to the IP asset 636 to theadditional computing device 630 and obtain input from the additionalcomputing device 630 indicating at least one product/service to economicdata mapping 626 to utilize to determine a valuation for the IP asset636.

FIG. 7 illustrates an example framework 700 to generate linguisticstructures for claims of patent documents according to someimplementations. The framework 700 includes an intellectual-propertyasset 702. In the illustrative example of FIG. 7, theintellectual-property asset 702 is a claim of a patent or patentapplication. At 704, a parsing and linguistic analysis 704 may beperformed with respect to the intellectual-property asset 702. Invarious implementations, the parsing and linguistic analysis 704 may beperformed by the intellectual-property services system 104. Inparticular implementations, the parsing and linguistic analysis 704 mayinclude identifying words of the intellectual-property asset 702 andcategorizing the words of the intellectual-property asset. Inillustrative examples, the parsing and linguistic analysis 704 maygenerate a linguistic analysis 706 for the intellectual-property asset702 that indicates parts of speech of at least a portion of the wordsincluded in the intellectual-property asset 702. For example, thelinguistic analysis 706 may indicate verbs, nouns, and adjectives of theintellectual-property asset 702. In additional scenarios, the linguisticanalysis 706 may also indicate adverbs, conjunctions, prepositions,pronouns, stop words, common words, unique words, or combinationsthereof, of the intellectual-property asset.

Additionally, the framework 700 may include, at 708, generating one ormore linguistic structures for the intellectual-property asset 702. Inparticular examples, the intellectual-property services system 104 maygenerate the one or more linguistic structures at 708. The one or morelinguistic structures may indicate relationships between words of theintellectual-property asset 702. In various implementations, multiplelinguistic structures may be generated for the intellectual-propertyasset 702. In illustrative implementations, a linguistic structure maybe generated for a plurality of features of the intellectual-propertyasset 702. For example, a linguistic structure may be generated foractions that are taking place in a claim. In certain implementations,the linguistic structures may be generated for individual elementsincluded in a claim of a patent or patent application.

In the illustrative example of FIG. 7, a linguistic structure 710 may begenerated for the feature of the intellectual-property asset 702starting with “displaying a portion of web page content . . . ”. Thisfeature may include one element of a claim of the intellectual-propertyasset 702. The linguistic structure 710 may be a tree structure thatincludes a root node 712 and a number of branch nodes 714, 716, 718. Theroot node 712 of the linguistic structure 710 includes the word“display”, which is a verb corresponding to the feature for which thelinguistic structure 710 is being generated. The nodes 714 and 716correspond to nouns that are related to the verb in the root node 712.Additionally, the node 718 corresponds to the noun and adjectiveincluded in the node 716. Although the illustrative example of thelinguistic structure 710 includes a single root node with three branchnodes, the linguistic structure 710, and other linguistic structures,may include additional nodes that correspond to different words of afeature of the intellectual-property asset 702. The root node 712 may beincluded in a first level of the linguistic structure 710, the secondnode 714 and the third node 716 may be included in a second level of thelinguistic structure 710, and the fourth node 718 may be included in athird level of the linguistic structure 710.

FIG. 8 illustrates an example framework 800 to determine a similaritymetric between a linguistic structure for a portion of a claim of apatent document and a linguistic structure of a product/serviceaccording to some implementations. The framework 800 includes thelinguistic structure 710 from FIG. 7 that represents a portion of aclaim of the intellectual-property asset 702. Additionally, at 802,linguistic structures may be generated for a number of products and/orservices using product/service data 804. The product/service data 804may include data that includes descriptions of products and/or services.The product/service data 804 may be analyzed and parsed using naturallanguage processing techniques to determine classifications for wordsincluded in the product/service data 804. Additionally, theproduct/service data 804 may be analyzed to generate linguisticstructures for various features of products included in theproduct/service data 804. For example, a first linguistic structure 806may be generated for at least one feature of a first product 808, asecond linguistic structure 810 may be generated for at least onefeature of a second product 812, and a third linguistic structure 814may be generated for at least one feature of a third product 816. Thelinguistic structures 806, 810, 814 may include tree structures with aroot node and one or more branch nodes.

At 818, the framework 800 may include determining similarity metrics 820between the linguistic structure 710 and the linguistic structures 806,810, 814. In various implementations, the similarity metrics 820 mayindicate an amount of similarity between linguistic structures. Thesimilarity metrics 820 may be determined based on similarities betweenwords included in the linguistic structure 710 and words included in thelinguistic structures 806, 810, 814. Additionally, the similaritymetrics 820 may be determined based on similarities between thearrangement of nodes included in the linguistic structure 710 and therespective arrangements of nodes included in the linguistic structures806, 810, 814. In particular, the similarity metrics 820 may include afirst similarity metric 822 that corresponds to an amount of similaritybetween the linguistic structure 710 and the first linguistic structure806. Additionally, a second similarity metric 824 may correspond to anamount of similarity between the linguistic structure 710 and the secondlinguistic structure 810. Further, the similarity metrics 820 mayinclude a third similarity metric 826 that corresponds to an amount ofsimilarity between the linguistic structure 710 and the third linguisticstructure 814. In various implementations, the similarity metrics 820may include numerical representations of amounts of similarity betweenlinguistic structures. In particular implementations, the similaritymetrics 820 may be designated along a numerical scale, such as 1 to 10or 1 to 100 or represented by a percentage that indicates amounts ofsimilarity between linguistic structures.

The amounts of similarity between the linguistic structure 710 and thelinguistic structures 806, 810, 814 may be used to determine one or moreof the products 808, 812, 816 that may correspond to theintellectual-property asset 702. That is, in situations where asimilarity metric 822, 824, 826 is greater than a threshold amount ofsimilarity, a mapping or other indicator of correspondence between theintellectual-property asset 702 and a respective product 806, 810, 814may be generated. The mappings may then be used to provide variousservices to organizations, such as IP valuations services, IPrisk-related services, and/or IP strategy-related services.

FIG. 9 illustrates an example framework 900 to a value of anintellectual property feature that corresponds to one or more productsaccording to some implementations. The framework 900 may include a firstproduct 902 that corresponds to a first IP feature 904 and a secondproduct 906 that corresponds to a second IP feature 908. The firstproduct 902 may be linked to the first IP feature 904 based on an amountof similarity between a linguistic structure of the product 902 and alinguistic structure of the first IP feature 904. Additionally, thesecond product 906 may be linked to the second IP feature 908 based onan amount of similarity between a linguistic structure of the secondproduct 906 and a linguistic structure of the second IP feature 908. Inillustrative examples, the first IP feature 904 may be an element of aclaim of a patent or patent application and the second IP feature may bean element of a claim of another patent or patent application.

The framework 900 also includes the intellectual-property servicessystem 104 and the intellectual-property knowledge data store 120. Theintellectual-property services system 104 may retrieve financial data910 from the intellectual-property knowledge data store 120. Thefinancial data 910 may include information related to revenue generatedby sales of various products and/or services, such as revenueinformation for the first product 902 and revenue information for thesecond product 906. The intellectual-property services system 104 mayalso, at 914, determine a portion of the value of a product and/orservice that corresponds to an IP feature associated with the productand/or service. For example, the intellectual-property services system104 may determine a portion of the amount of revenue of the firstproduct 902 to attribute to the first IP feature 904. In variousimplementations, the amount of revenue of the first product 902 toattribute to the first IP feature 904 may be based on a measure ofbreadth of the IP feature 904. To illustrate, the intellectual-propertyservices system 104 may determine a breadth of the first IP feature 904with respect to additional intellectual property features included in asame technology classification as the first IP feature 904. Based on themeasure of breadth of the first IP feature 904 in relation to thebreadth of other IP features, the intellectual-property services system104 may determine an amount of revenue of the first product 902 toattribute to the first IP feature 904. In certain situations, the higherthe value of the measure of breadth of the first IP feature 904, thehigher the percentage of revenue of the first product 902 to attributeto the first IP feature 904. Further, the lower the value of the measureof breadth of the first IP feature 904, the lower the percentage ofrevenue of the first product 902 to attribute to the first IP feature904.

At 914, the framework 900 includes determining a value 916 for the IPfeature 914. In particular implementations, the intellectual-propertyservices system 104 may determine the value 916 of the first IP feature904 based on the amount of revenue of the first product 902 and aportion of the revenue of the first product 902 attributed to the firstIP feature 904. In various implementations, the intellectual-propertyservices system 104 may multiple the portion of the revenue of the firstproduct 902 attributed to the first IP feature 904 by revenueinformation of the first product 902 to determine the value 916 of thefirst IP feature 904.

FIG. 10-14 illustrate example processes of analyzingintellectual-property data. The processes described herein areillustrated as collections of blocks in logical flow diagrams, whichrepresent a sequence of operations, some or all of which may beimplemented in hardware, software or a combination thereof. In thecontext of software, the blocks may represent computer-executableinstructions stored on one or more computer-readable media that, whenexecuted by one or more processors, program the processors to performthe recited operations. Generally, computer-executable instructionsinclude routines, programs, objects, components, data structures and thelike that perform particular functions or implement particular datatypes. The order in which the blocks are described should not beconstrued as a limitation, unless specifically noted. Any number of thedescribed blocks may be combined in any order and/or in parallel toimplement the process, or alternative processes, and not all of theblocks need be executed. For discussion purposes, the processes aredescribed with reference to the environments, architectures and systemsdescribed in the examples herein, such as, for example those describedwith respect to FIGS. 1-9, although the processes may be implemented ina wide variety of other environments, architectures and systems.

FIG. 10 illustrates an example process 1000 to determine anintellectual-property asset that corresponds to a product or serviceaccording to some implementations.

At 1002, the process 1000 includes receiving, from one or more datasources, information about products. In particular implementations, theone or more data sources may include a publicly accessible data source.Publicly accessible data sources may include websites that includeinformation that may be accessed by the general public withoutcredentials issued by the organizations maintaining and/or controllingaccess to the websites. For example, publicly accessible data sourcesmay include uniform resource locators (URLs) that are available to thepublic without the URL(s) being first provided to individuals by theorganizations themselves. In contrast, access to private data sourcesmay be controlled more strictly than access to public data sources byrestricting access to URLs associated with the private data sourcesand/or by requiring specific credentials to access the private datasources. In some situations, an organization may maintain and/or controla website that includes both publicly accessible information that may beaccessible to the general public and privately accessible informationthat is accessible to customers, employees, and other individuals thathave specifically been granted access by the organization. The publiclyaccessible data sources may include government websites,intellectual-property databases maintained by intellectual propertyjurisdictions, websites of companies offering products and/or services,combinations thereof, and the like. In these situations, obtaining datarelated to a product and/or service from the publicly accessible datasource may include determining a plurality of keywords associated withthe product and/or service and parsing the publicly accessible datasource to identify data that corresponds to at least one keyword of theplurality of keywords. Additionally, the data that corresponds to the atleast one keyword may be extracted from the publicly accessible datasource and the data that corresponds to the at least one keyword may bestored in a data store of a service provider. In certainimplementations, an intellectual-property services provider may obtaininformation from public data sources using web crawlers or otherapplications that may identify websites and parse the websites forspecified information.

In additional implementations, the one or more data sources may includea data source of an organization that offers a product and/or servicefor acquisition. A data source of an organization may be a private datasource that is accessible to an intellectual-property services providerbased at least partly on the organization granting access by theintellectual-properly service provider to the data source of theorganization. The data source of the organization may be accessible viaa database management application and an intellectual-property servicesprovider may utilized the database management application to parse thedata source of the organization for at least one keyword of a pluralityof keywords associated with a product and/or service and to extract datacorresponding to the at least one keyword from the data source of theorganization. The intellectual-property services provider may then storethe data obtained from the data source of the organization in a datastore of an additional organization, such as a data store of theintellectual-property services provider. In various implementations,data related to a product and/or service that is stored in a data storeof an organization that is offering the product and/or service for salemay be stored such that relationships between intellectual-propertyassets and respective product and/or services are identifiable. That is,the organization may have tracked the intellectual-property assets thatare associated with particular products and/or services and stored dataindicating this relationship. In this way, an intellectual-propertyservices provider may search a data store of an organization andidentify intellectual-property assets that correspond to products and/orservices offered for sale by the organization using data generated bythe organization.

In further implementations, data relating to products and/or servicesmay be obtained using crowdsourcing techniques. To illustrate, anintellectual-property services provider may cause a request forinformation about a product and/or service to be published on a website.Individuals accessing the website may submit responses to the requestvia the website. In additional implementations, an intellectual-propertyservices provider may send requests to particular individuals to obtaininformation about products and/or services. The requests may be includedin one or more types of communication, such as email, mobile devicemessage, instant messaging notification, phone call, combinationsthereof, and so forth In various implementations, theintellectual-property services provider may identify one or more groupsof individuals to obtain information about one or more products and/orservices. For example, the intellectual-property services provider mayidentify individuals that may be considered experts and/or have at leasta threshold amount of knowledge about various products and/or servicesand the intellectual-property services provider may contact a respectivegroup of individuals when the intellectual-property services providerwould like to obtain information about a product and/or service forwhich the respective group has knowledge. In these scenarios, at least aportion of the individuals contacted by the intellectual-propertyservices provider may provide information about one or more productsand/or services to the intellectual-property services provider inresponse to the request(s). In some cases, information obtained about aproduct and/or service may indicate one or more sources of informationabout the product and/or service, such as one or more websites orpublications that may include information about the product and/orservice. Additionally, information about a product and/or service mayinclude at least one of a description of the product and/or service,pricing information related to the product and/or service, financialinformation of the product and/or service.

In various implementations, an intellectual-property services providermay also provide one or more portals for individuals to submitinformation. For example, the intellectual-property services providermay generate one or more user interfaces that include at least one userinterface element to capture information about products and/or servicesoffered for sale by an organization and/or to capture information aboutintellectual-property assets of the organization. The portals may beaccessible by representatives of at least one of theintellectual-property services provider or the organization. Inparticular implementations, the intellectual-property services providermay provide a portal that may be used to obtain information about tradesecrets of the organization. In additional implementations, theintellectual-property services provider may provide a portal that may beused to obtain information about patent documents of the organization.In further implementations, the intellectual-property services providermay provide a portal that may be used to obtain information aboutproducts and/or services offered by the organization.

At block 1004, the process 1000 includes identifyingintellectual-property assets. For example, the intellectual-propertyassets may be identified from publicly-available resources and/or fromresources associated with one or more organizations.

At 1006, the process 1000 includes determining one or more relationshipsbetween individual ones of the products and individual ones of theintellectual-property assets. The relationships between the individualproducts or services and the individual intellectual-property assets maybe determined by identifying features of the products or services andfeatures of the intellectual-property assets. The features of theproducts or services may be determined by parsing descriptions of theproducts or services and identifying functional features, physicalfeatures, and/or technical features of the products or services. Invarious implementations, videos and/or images related to the products orservices may be analyzed using one or more object recognition techniquesto determine features of the products or services. Anintellectual-property services provider may analyze features of theintellectual-property asset and features of products or services todetermine similarities between the features of the products or servicesand the features of the intellectual-property asset. In some cases, theamount of similarity may be based on similarities of words associatedwith the products and or services and the intellectual-property asset.The amount of similarity may also be based on similarities inrelationships between words related to features of the products orservices and relationships between words related to features of theintellectual-property asset. The intellectual-property services providermay determine that there is a relationship between a product or serviceand an intellectual-property asset based on a similarity between thefeatures of the product or service and the features of theintellectual-property asset is at least a threshold similarity. In anillustrative example, an intellectual-property services provider maydetermine features of a claim of a patent document and features of aproduct or service. The intellectual-property service provider may thenidentify a relationship between the claim and the product or servicebased on similarities between the features of the claim of the patentdocument and features of the product or service.

At 1008, the process 1000 includes generating, based at least in part onthe one or more relationships, association data indicating the one ormore relationships between the individual ones of the products and theindividual ones of the intellectual-property assets. For example, theassociation data may include a framework of relationships betweenindividual products or services and at least one intellectual-propertyasset that is mapped to the product or service. The framework may alsoindicate individual intellectual-property assets and at least oneproduct or service that is associated with the intellectual-propertyasset. In this way, the framework may be searchable based onintellectual-property asset or based on product or service in order toidentify products or services and intellectual-property assets that arerelated.

The association data may include a mapping included in the frameworkthat indicates an intellectual-property asset that corresponds to aproduct or service. In various implementations, an intellectual-propertyservices provider may receive a request to identify one or more productsor services and one or more intellectual-property assets that arerelated. In these situations, the intellectual-property servicesprovider may parse the framework based on identifiers of theintellectual-property assets or identifiers of the intellectual-propertyassets to determine relationships between products or services andintellectual-property assets. The relationships between the products orservices and the intellectual-property assets may be utilized by theintellectual-property services provider to provide various intellectualproperty related services to a customer of the intellectual-propertyservices provider. In particular implementations, the intellectualproperty related services may include valuation services forintellectual-property assets. In these scenarios, theintellectual-property services provider may determine one or moremetrics related to the intellectual-properly asset where the one or moremetrics including at least one of a measure of breadth of one or moreportions of the intellectual-property asset, a measure of risk withrespect to the one or more portions of the intellectual-property asset,or a measure of coverage of the one or more portions of theintellectual-property asset. The intellectual-property services providermay also determine revenue obtained for the product or service over aperiod of time and then determine, based at least partly on the one ormore metrics, an amount of the revenue of the product or service toattribute to one or more portions of the intellectual-property asset.After determining the amount of revenue of the product or service toattribute to the intellectual-property asset, the intellectual-propertyservices provider may determine a value of the intellectual-propertyasset based at least partly the amount of revenue of the product orservice obtained over the period of time and the portion of the amountof revenue of the product or service attributed to theintellectual-property asset.

At block 1010, the process 1000 includes receiving a request to identifyan intellectual-property asset of the intellectual-property assets thatcorresponds to a product of the products. For example, a user, using auser interface, may provide input indicating a request to identify anasset that corresponds to a given product of the products. Input datacorresponding to the input may be received as the request.

At block 1012, the process 1000 includes identifying, based at least inpart on the association data, the intellectual-property asset thatcorresponds to the product. For example; the system may be utilized todetermine, using the association data, which products have relationshipswith the intellectual-property asset.

At 1014, the process 1000 includes generating a response to the request;the response indicating that the intellectual-property asset isassociated with the product. In some implementations, the user interfacemay also include one or more user interface elements to provide inputregarding the relationships between the intellectual-property asset andthe product or service. In certain implementations, input may beobtained via the user interface or via an additional user interfaceindicating one or modifications to the relationship between theintellectual-property asset and the product or service.

FIG. 11 illustrates an example process 1100 to determine anintellectual-property asset that corresponds to a product or serviceusing a classification system according to some implementations.

At 1102, the process 1100 includes generating a classification systemthat includes classifications, individual ones of the classificationscorresponding to a technology group. In various implementations,individual classifications may be associated with one or more criteria.In illustrative implementations, the individual classifications may beassociated with one or more words and each classification may beassociated with different groups of words. Additionally, the individualclassifications of the classification system may be associated with oneor more physical features, one or more technical features, orcombinations thereof. In certain implementations, the one or morephysical features and/or the one or more technical features may each berelated to a set of words.

At 1104, the process 1100 includes receiving information about a productoffered for acquisition by an organization, the information obtainedfrom at least one of: a datastore of the organization; a website of theorganization; or input via a user interface.

At 1106, the process 1100 includes determining, based at least in parton the information, a first feature of the product. The informationabout the product or service may be analyzed by parsing the informationabout the product or service to determine one or more words associatedwith the product or service. In particular implementations, theinformation about the product or service may be analyzed to determine atleast one of one or more physical features or one or more technicalfeatures of the product or service. The one or more physical featuresand/or the one or more technical features of the product or service maybe identified based at least partly on comparing words of at least onetechnical feature and/or words of at least one physical feature to wordsincluded in the information obtained about the product or service. In anillustrative implementation, a physical feature of the product orservice may be identified based at least partly on at least one wordrelated to the physical feature being included in the information aboutthe product or service. In addition, a technical feature of a product orservice may be identified based at least partly on at least one wordrelated to the technical feature being included in the information aboutthe product or service.

At 1108, the process 1100 includes determining that the productcorresponds to a classification of the classifications based at leastpartly on the first feature corresponding to a reference featureassociated with the classification. In various implementations, wordsassociated with the first features of the product or service may becompared to additional words associated with second features of theclassification. In certain implementations, a classification may beassigned to a product or service based at least partly on at least athreshold number of words of the first features of the product orservice corresponding to a number of words of the second features of theclassification. In particular implementations, a model may be used todetermine classifications for product or services. The model may receiveinput including words corresponding to features of products or serviceand words corresponding to classifications and determine probabilitiesthat products or services correspond to classifications of theclassification system. In illustrative implementations, a classificationmay be assigned to a product or service when a probability that theproduct or service corresponds to a classification is greater than athreshold probability. In additional implementations, a classificationmay be assigned to a product or service when a probability that theproduct or service corresponds to the classification is a highestprobability among a plurality of probabilities that have been determinedfor the product or service using the model for a plurality ofclassifications.

At 1110, the process 1100 includes identifying an intellectual-propertyasset associated with the organization. The intellectual-property assetof the organization may be identified based on information obtained fromthe organization. In particular implementations, anintellectual-property services provider may obtain information about theintellectual-property asset that includes a document that corresponds tothe intellectual-property asset, such as a trade secret document, apatent application, a utility patent, a design patent, a plant patent, atrademark application, or a copyright submission. In additionalimplementations, an organization may provide identifiers ofintellectual-property assets of the organization and theintellectual-property services provider may obtain information about theintellectual-property assets from one or more databases based on theidentifiers.

At 1112, the process 1100 includes determining a second feature of theintellectual-property asset. The features of the intellectual-propertyasset may be determined by analyzing information related to theintellectual-property asset, such as documents related to theintellectual-property asset. In particular implementations, theintellectual-property asset may be a claim of a patent or patentapplication, and the features of the intellectual-property asset may beidentified by analyzing words of the claim. Additionally, when theintellectual-property asset is a claim of a patent or patentapplication, the features of the intellectual-property asset may beidentified by analyzing words of elements of the claim. Further, whenthe intellectual-property asset is a trademark, the features of thetrademark may be identified by analyzing words of a description of goodsor services associated with the trademark. In various implementations,the features of the intellectual-property asset may be identified bycomparing words included in documents associated with theintellectual-property asset with words associated with physical featuresand/or technical features. An intellectual-property services providermay assign words to individual physical features and individualtechnical features. In certain implementations, an intellectual-propertyservices provider may determine that an intellectual-property assetincludes a technical feature or a physical feature when at least oneword associated with the intellectual-property asset corresponds to atleast one additional word related to the technical feature or at leastone additional word related to the physical feature.

At 1114, the process 1100 includes determining that theintellectual-property asset corresponds to the classification based atleast partly on the second feature of the intellectual-property assetcorresponding to the reference feature associated with theclassification. An intellectual-property services provider may determinethat one or more third features of the intellectual-property assetcorrespond to the at least one fourth feature associated with theclassification by comparing words of the one or more third features towords of the at least one fourth feature. In various implementations,the intellectual-property services provider may determine that the oneor more third features of the intellectual-property asset correspond tothe at least one fourth feature based at least partly on at least athreshold number of words of the one or more third features correspondto words of the at least one fourth feature.

In particular implementations, a model may be used to determineclassifications for intellectual-property assets. The model may receiveinput including words corresponding to features of intellectual-propertyassets and words corresponding to classifications and determineprobabilities that intellectual-property assets correspond toclassifications of the classification system. In illustrativeimplementations, a classification may be assigned to anintellectual-property asset when a probability that theintellectual-property asset corresponds to the classification is greaterthan a threshold probability. In additional implementations, aclassification may be assigned to an intellectual-property asset when aprobability that the intellectual-property asset corresponds to theclassification is a highest probability among a plurality ofprobabilities that have been determined for the intellectual-propertyasset using the model for a plurality of classifications.

In various implementations, models used to determine classifications forintellectual-property assets and models used to determineclassifications for intellectual-property assets may be modified. Forexample, an intellectual-property services provider may request inputregarding a classification of an intellectual-property asset. In somecases, the input may indicate that the intellectual-property assetshould be classified according to a different classification. In othersituations, the input may indicate that the intellectual-property assetis classified correctly. The intellectual-property services provider maythen modify the model used to classify intellectual-property asset basedthe input. Additionally, the intellectual-property services provider mayrequest input regarding a classification of a product or service. Theinput may indicate that the product or service should be classifiedaccording to a different classification. In other scenarios, the inputmay indicate that the product or service is classified correctly. Theintellectual-property services provider may then modify the model usedto classify the product or service based on the input.

FIG. 12 illustrates an example process 1200 to perform a qualitativeanalysis and a quantitative analysis of intellectual-property dataaccording to some implementations.

At 1202, the process 1200 includes receiving information indicatingrevenue associated with a product. The information may include financialdata such as information regarding revenue obtained by one or moreorganizations through sales of the product or service. The financialdata may be obtained from a variety of sources. For example, anintellectual-property services provider may provide a portal thatcaptures information regarding financial data of products and/orservices. To illustrate, the intellectual-property services provider maygenerate one or more user interfaces that include one or more userinterface elements to capture one or more portions of the financialdata. In additional implementations, the intellectual-property servicesprovider may implement software tools to parse a datastore of anorganization that offers the product or service for sale to identifyportions of the financial data corresponding to the product or service.In further implementations, the intellectual-property services providermay analyze information from one or more websites to identify at least aportion of the financial data corresponding to the product or service.In illustrative examples, the intellectual-property services providermay utilize web crawlers and other website parsing tools to analyzeinformation included in websites, including websites of one or moreorganizations offering the product or service for acquisition and/orthird-party websites, to identify at least a portion of the financialdata corresponding to the product or service.

At block 1204, the process 1200 includes determining a classification ofthe product based at least partly on a technical feature of the product.For example, the intellectual-property services provider may determine aclassification for the product or service by determining features of theproduct or service and comparing the features of the product or serviceto criteria for a number of classifications of the classificationsystem.

At block 1206, the process 1200 includes identifying a patent claim thatcorresponds to the product based at least partly on the patent claimbeing associated with the classification. For example,intellectual-property assets may be associated with the classification.Those intellectual-property assets may include patents, which mayinclude claims. Additionally, the process 1200 may include identifying,generally, an intellectual-property asset of an organization. Theintellectual-property asset of the organization may include one or moreintellectual-property assets having legal rights that may be enforced bythe organization. In various implementations, the intellectual-propertyasset may be assigned to the organization. In additionalimplementations, the organization may have a license with respect to theintellectual-property asset. An intellectual-property services providermay determine that the intellectual-property asset corresponds to theorganization based at least partly on information obtained from theorganization. For example, the organization may provide a list ofintellectual-property assets to the intellectual-property servicesprovider. The list may be stored in a data store of the organizationthat is accessible to the intellectual-property services provider andthe intellectual-property services provider may parse the data store toobtain the list. In further implementations, the organization mayprovide the list of intellectual-property assets to theintellectual-property services provider via a communication, such as anemail or message. Also, the intellectual-property services organizationmay provide a customer portal by which the organization may provide alist of intellectual-property assets of the organization. In particularimplementations, the intellectual-property services provider may analyzeinformation available from public data sources, such as patentjurisdiction databases, to identify intellectual-property assets of theorganization. To illustrate, the intellectual-property services providermay parse a publicly accessible datastore to identifyintellectual-property assets that are assigned to the organization,intellectual-property assets where the organization is an applicant,intellectual-property assets having inventors that are related to theorganization, or combinations thereof.

At block 1208, the process 1200 includes identifying words included inthe patent claim. For example, the data representing the patent may beparsed and/or textual recognition techniques may be performed toidentify the words that make up the patent claim.

At block 1210, the process 1200 includes determining a breadth of thepatent claim. In some implementations, the intellectual-propertyservices provider may determine the breadth of the intellectual-propertyasset relative to the breadth of other intellectual-property assets,such as intellectual-property assets in a same classification as theintellectual-property asset, to determine the portion of revenue of theproduct or service to attribute to the intellectual-property asset.

At block 1212, the process 1200 includes determining a portion of therevenue to apportion to the patent claim based at least partly on thebreadth of the patent claim. For example, in order to determine themeasures of breadth and/or portions of revenue of the products and/orservices corresponding to the intellectual-property assets, theintellectual-property services system may utilize one or more linguisticanalysis techniques and one or more machine learning techniques. Anintellectual-property services provider may determine a portion of therevenue for the product or service to attribute to theintellectual-property asset based on an amount of features of theproduct or service that are covered by the intellectual-property asset.For example, if a product or service has a number of features, theportion of the number of features covered by the intellectual-propertyasset with respect to the total number of features may correspond to theportion of the revenue for the product or service to attribute to theintellectual-property asset. In an illustrative example, anintellectual-property asset may cover 2% of the features of a product orservice, and the intellectual-property services provider may determinethat 2% of the revenue of the product or service is to be attributed tothe intellectual-property asset. In particular implementations, theproportion of the features of the product or service covered by theintellectual-property asset may serve as a starting point fordetermining the portion of revenue for the product or service toattribute to the intellectual-property asset. In variousimplementations, an intellectual-property services provider may modifyan initial portion of the amount of revenue of the product or serviceattributed to the intellectual-property asset based on a number ofdiscount factors, which will be discussed in more detail below. Inadditional implementations, an intellectual-property services providermay determine a portion of the revenue for the product or service toattribute to the intellectual-property asset based on a breadth of theintellectual-property asset. In some implementations, theintellectual-property services provider may determine the breadth of theintellectual-property asset relative to the breadth of otherintellectual-property assets, such as intellectual-property assets in asame classification as the intellectual-property asset, to determine theportion of revenue of the product or service to attribute to theintellectual-property asset.

At block 1214, the process 1200 includes determining a measure of valueof the patent claim based at least partly on the portion of the revenueapportioned to the patent claim. For example, the measure of value forthe intellectual-property asset may be determined by multiplying therevenue for the product or service by the portion of the revenue of theproduct or service attributed to the intellectual-property asset. Invarious implementations, one or more discount factors may also be usedto determine the measure of value for the intellectual-property asset.The discount factors may be applied to at least one of the amount ofrevenue for the product or service used to determine the measure ofvalue or the portion of the revenue for the product or serviceattributed to the intellectual-property asset. The one or more discountfactors may reduce an initial measure of the value of theintellectual-property asset to a modified measure of value of theintellectual-property asset. In illustrative examples, one or morediscount factors may be based at least partly on a first riskcorresponding to invalidation of the intellectual-property asset and asecond risk corresponding to a probability of litigation with respect tothe intellectual-property asset. In particular implementations, theintellectual-property asset may include a patent claim and the firstrisk may be based at least partly on prosecution history events relatedto the patent claim. Additionally, in situations where theintellectual-property asset includes a patent claim, the first risk maybe based at least partly on metrics of an examiner related to the patentclaim relative to additional metrics of additional examiners included ina same art unit as the examiner, the metrics corresponding to at leastone of a number of notices of allowance produced over a period of time,an average number of office actions before producing a notice ofallowance, a number of notices of appeal filed over the period of time,a number of reversals in appeal decisions over the period of time, orcombinations thereof. Further, the second risk based at least partly ona first number of litigation events taking place with respect to anumber of intellectual-property assets having a same classification asthe intellectual-property asset relative to a second number oflitigation events taking place with respect to an additional pluralityof intellectual-property assets included in a different classificationof a classification system. In some illustrative examples where theintellectual-property asset includes a patent claim, a discount factormay be determined based at least partly on a number of additional patentclaims assigned to the organization that correspond to the product orservice. In illustrative examples where the intellectual-property assetincludes a trademark, a discount factor may be based at least partly onat least one of a number of litigation events related to trademarkassets included in a same classification as the trademark asset, anumber of oppositions related to the trademark assets included in thesame classification as the trademark asset, or metrics of an examinerassociated with the trademark asset in relation to additional metrics ofadditional examiners associated with additional trademark assetsincluded in the classification.

Additionally, or alternatively, the process 1200 may include determiningthat the product or service corresponds to the intellectual-propertyasset. An intellectual-property services provider may determine that theproduct or service corresponds to the intellectual-property asset basedon obtaining input indicating that the product or service corresponds tothe intellectual-property asset. For example, a representative of theorganization may access a customer portal provided by theintellectual-property services provider to enter information via a userinterface indicating that the product or service corresponds to theintellectual-property asset. In other examples, a representative of theintellectual-property services provider may enter information into auser interface indicating that the intellectual-property assetcorresponds to the product or service. In additional implementations,the organization may store data indicating relationships betweenintellectual-property assets and products and/or services offered by theorganization for sale. To illustrate, for each product or service of theorganization, the organization may store a list of intellectual-propertyassets that are related to one or more features of the respectiveproduct or service. In these scenarios, the intellectual-propertyservices provider may parse a datastore of the organization or a websiteof the organization that includes the list of intellectual-propertyassets that are related to one or more products and/or services of theorganization.

In additional implementations, an intellectual-property servicesprovider may determine a product or service that corresponds to anintellectual-property asset of the organization by determining an amountof similarity between the product or service and theintellectual-property asset. In various implementations, theintellectual-property service provider may parse anintellectual-property document associated with the intellectual-propertyasset to determine individual first words of the intellectual-propertydocument and parse information related to the product or service todetermine individual second words included in the information. Theintellectual-property services provider may then determine a similaritymetric between at least a portion of the individual first words and atleast a portion of the individual second words. Theintellectual-property services provider may determine that the productor service corresponds to the intellectual-property asset based at leastpartly on determining that the similarity metric is at least a thresholdsimilarity metric. In further implementations, the intellectual-propertyservices provider may analyze information about the product or serviceand information about the intellectual-property asset to determinephysical and/or technical features of the product or service andphysical and/or technical features of the intellectual-property asset.The intellectual-property services provider may determine that theintellectual-property asset corresponds to the product or service basedat least partly on similarities between physical features and/ortechnical features of the product or service and physical featuresand/or technical features of the intellectual-property asset.

In particular implementations, the intellectual-property servicesprovider may determine that both the product or service and theintellectual-property asset are associated with a same classification ofa classification system before analyzing the information of the productor service and the information of the intellectual-property asset todetermine similarities between the intellectual-property asset and theproduct or service. In various implementations, theintellectual-property services provider may determine a classificationfor the product or service by determining features of the product orservice and comparing the features of the product or service to criteriafor a number of classifications of the classification system.Additionally, the intellectual-property services provider may determinea classification for the intellectual-property asset by determiningfeatures of the intellectual-property asset and comparing the featuresof the intellectual-property asset to criteria for a number ofclassifications of the classification system. In variousimplementations, the intellectual-property services provider maydetermine first similarity metrics indicating amounts of similaritybetween the features of the product or service and the criteria of theclassifications and determine second similarity metrics indicatingamounts of similarity between the features of the intellectual-propertyasset and the criteria of the classifications. The intellectual-propertyservices provider may then utilize the first similarity metrics todetermine a classification for the product or service and the secondsimilarity metrics to determine a classification for theintellectual-property asset. The intellectual-property services providermay determine a classification for the product or service and aclassification for the intellectual-property asset based on a thresholdsimilarity metric such that a first similarity metric and/or a secondsimilarity metric for a particular classification that is above thethreshold similarity metric may indicate that product or service and/orthe intellectual-property asset corresponds to the classification. Inadditional implementations, the intellectual-property services providermay determine a first similarity metric having a highest value among thefirst similarity metrics to determine that the classification related tothe highest value first similarity metric corresponds to the product orservice. The intellectual-property services provider may also determinea second similarity metric having a highest value among the secondsimilarity metrics to determine that the classification related to thehighest value second similarity metric corresponds to theintellectual-property asset.

FIG. 13 illustrates an example process 1300 to determine anintellectual-property asset that corresponds to a product or serviceusing a linguistic structure of the intellectual-property asset and alinguistic structure of the product or service according to someimplementations.

At 1302, the process 1300 includes determining first parts of speech forfirst words included in first information associated with a product. Invarious implementations, natural language processing techniques may beused to determine the individual words included in the first informationand the parts of speech associated with the individual words. Inparticular implementations, an intellectual-property services providermay determine at least one of nouns, verbs, adjectives, adverbs,prepositions, conjunctions, or pronouns included in the firstinformation. Additionally, the intellectual-property services providermay determine relationships between the words included in the firstinformation. For example, the intellectual-property services providermay identify the words included in a same sentence. Theintellectual-property services provider may also identify words includedin a same paragraph. Additionally, the intellectual-property servicesprovider may identify one or more adjectives that modify individualnouns and one or more adverbs that modify individual verbs. Further, theintellectual-property services provider may store data indicating therelationships between words. To illustrate, the intellectual-propertyservices provider may assign an identifier to individual words includedin the first information and assign codes or classes to the individualwords. In a particular example, the intellectual-property servicesprovider may assign a code to a word included in the first informationindicating that the word is a noun and also store in a table related tothe word, an identifier of an adjective related to the word. The tablemay also include identifiers of words in a same sentence or element asthe noun.

At block 1304, the process 1300 includes determining second parts ofspeech for second words included in second information corresponding toa claim of a patent document. Determining the second parts of speech maybe performed in the same or a similar manner as determining the firstparts of speech, as described above.

At block 1306, the process 1300 includes determining a portion of thefirst words that correspond to a feature of the product. For example, acatalog of features may be associated with the product, and theintellectual-property services provider may analyze the first words inassociation with the features to determining which of the wordscorrespond to at least one of the features associated with the product.

At block 1308, the process 1300 includes determining, based at leastpartly on the first parts of speech, a first action performed withrespect to the feature. For example, the intellectual-property servicesprovider may determine which of the words is a verb acting on a givenfeature. The verb may indicate the action performed with respect to thefeature.

At 1310, the process 1300 includes generating, based at least partly onthe first action, a first linguistic structure for the feature, thefirst linguistic structure indicating one or more first relationshipsbetween the first action and one or more first nouns included in thefirst information. In particular examples, the linguistic structure mayinclude a tree structure with a root node and one or more branch nodes.The root node may be in a first level of the tree structure and the oneor more branch nodes may be included in subsequent levels of the treestructure. In the tree structure, each node that is a branch of anothernode is related to the initial node. That is, the tree structure mayinclude parent nodes and child nodes that are related to the parentnodes. In an illustrative example, a noun included in a first node on afirst level of the tree structure may be associated with a firstadjective included in a second node and a second adjective in a thirdnode of the tree structure, where the second node and third node arechild nodes of the first node and are included in a second level of thetree structure. In various implementations, a linguistic structure ofthe intellectual-property asset may be generated with respect to anaction related to the intellectual-property asset in a root node withthe words corresponding to the action being included in the branchnodes. In an illustrative example, a verb corresponding to the actionmay be included in a root node on a first level of the linguisticstructure and nouns and adjectives related to the verb may be includedin branch nodes of the linguistic structure on second and/or thirdlevels of the linguistic structure. In situations where theintellectual-property asset is a patent claim, the intellectual-propertyservices provider may generate a linguistic structure for individualelements included in the patent claim.

At 1312, the process 1300 includes determining, based at least partly onthe second parts of speech, a second action included in the claim. Thatis, an intellectual-property services provider may analyze the wordsincluded in the second information and identify at least one of nouns,verbs, adjectives, adverbs, prepositions, conjunctions, or pronounsincluded in the second information. In particular implementations, theintellectual-property services provider may utilize natural languageprocessing techniques to determine the individual words and therespective parts of speech of the words included in the secondinformation.

At 1314, the process 1300 includes generating, based at least partly onthe second action, a second linguistic structure for the claim, thesecond linguistic structure indicating one or more second relationshipsbetween the second action and one or more second nouns included in theclaim. The second and/or additional linguistic structure generated basedon the second information may have a tree structure with a root node andone or more branch nodes. The root node may be in a first level of thetree structure and the one or more branch nodes may be included insubsequent levels of the tree structure. In the tree structure, eachnode that is a branch of another node is related to the initial node.That is, the tree structure may include parent nodes and child nodesthat are related to the parent nodes. In an illustrative example, a nounincluded in a first node on a first level of the tree structure may beassociated with a first adjective included in a second node and a secondadjective in a third node of the tree structure, where the second nodeand third node are child nodes of the first node and are included in asecond level of the tree structure. In various implementations, alinguistic structure of the product or service may be generated withrespect to an action performed with respect to the product or servicebeing in a root node with the additional words corresponding to theaction being included in the branch nodes. In an illustrative example, averb corresponding to the action may be included in a root node on afirst level of the additional linguistic structure and nouns andadjectives related to the verb may be included in branch nodes of theadditional linguistic structure on second and/or third levels of theadditional linguistic structure. In some implementations, theintellectual-property services provider may generate a linguisticstructure for individual technical features of the product or service,for individual physical features of the product or service, or both.

At 1316, the process 1300 includes determining a similarity metricbetween the first linguistic structure and the second linguisticstructure. For example, one or more components of the first linguisticstructure may be compared to one or more components of the secondlinguistic structure. When the components of the linguistic structurescorrespond to each other, the similarity metric may indicate a highdegree of similarity. When the components do not correspond and/ordifferences exist as between the linguistic structures, the similaritymetric may indicate a low degree of similarity. The measure ofsimilarity between the first linguistic structure and the secondlinguistic structure may be determined by comparing similarities in theconfiguration of the first linguistic structure and the configuration ofthe second linguistic structure. For example, an intellectual-propertyservices provider may determine the measure of similarity based on anumber of levels included in the first linguistic structure and a numberof levels included in the second linguistic structure. Theintellectual-property services provider may also determine the measureof similarity based on a number of nodes in each level of the firstlinguistic structure and a number of nodes in each level of the secondlinguistic structure. To illustrate, the intellectual-property servicesprovider may compare a number of nodes in a second level of the firstlinguistic structure with a number of nodes in a second level of thesecond linguistic structure.

The intellectual-property services provider may also determine themeasure of similarity based on similarities between words included inthe first linguistic structure and the words included in the secondlinguistic structure. To illustrate, the intellectual-property servicesprovider may compare one or more words included in a root node of thefirst linguistic structure with one or more words included in a rootnode of the second linguistic structure. In these situations, themeasure of similarity may be based on whether the one or more wordsincluded in the root node of the first linguistic structure and the oneor more words included in the root node of the second linguisticstructure are the same, similar, synonyms, and the like. Additionally,the intellectual-property services provider may compare words in thebranch nodes of the first linguistic structure and the words in thebranch nodes of the second linguistic structure to determine the measureof similarity. In particular implementations, the intellectual-propertyservices provider may compare words included in individual levels of thefirst linguistic structure with words included in individual levels ofthe second linguistic structure.

At 1318, the process 1300 includes determining, based at least partly onthe similarity metric, that the claim corresponds to the product. Insome illustrative examples, the intellectual-property services providermay determine that the product or service and the intellectual-propertyasset are in a same classification of a classification system beforecomparing the first linguistic structure and the second linguisticstructure. Additionally, in various situations, theintellectual-property service provider may generate multiple linguisticstructures for the product or service and multiple linguistic structuresfor the intellectual-property asset. In these scenarios, theintellectual-property services provider may compare one or morelinguistic structures of the product or service with one or morelinguistic structures of the intellectual-property asset to determine ameasure of similarity between the product or service and theintellectual-property asset. In further implementations, the measure ofsimilarity, such as a similarity metric, between the first linguisticstructure and the second linguistic structure may be modified based onuser input. For example, the intellectual-property services provider mayreceive input indicating that the product or service does not correspondwith the intellectual-property asset. In these situations, theintellectual-property services provider may modify the measure ofsimilarity and/or modify a model used to generate the measure ofsimilarity based on the input. In additional implementations, theintellectual-property services provider may determine that anintellectual-property asset and a product or service do not correspondto each other and the intellectual-property services provider mayreceive input indicating that the additional product or service and theadditional intellectual-property asset do correspond to one another.Accordingly, the intellectual-property services provider may modify anadditional measure of similarity between one or more linguisticstructures of the product or service and one or more linguisticstructures of the intellectual-property asset or a mode used to generatethe additional measure of similarity based on the input.

FIG. 14 illustrates an example process 1400 to provide services to acustomer based on relationships between a product or service and anintellectual-property asset according to some implementations.

At 1402, the process 1400 includes receiving, from one or more datasources, first information regarding products offered for acquisition.The information may include details associated with the products and/orthe sources of the products, for example.

At 1404, the process 1400 includes receiving, from the one or more datasources, second information regarding intellectual-property assets. Forexample, the information may include documents and/or data associatedwith the intellectual-property assets and/or that correspond to theintellectual-property assets.

At 1406, the process 1400 includes determining, based at least in parton the first information and the second information, that anintellectual-property asset of the intellectual-property assetscorresponds to a feature associated with a product of the products. Thecomparisons between the individual intellectual-property assets and theindividual products and/or services may be used by theintellectual-property services provider to determine similarity metricsbetween the individual intellectual-property assets and the individualproducts and/or services. In situations, where a similarity metric isgreater than a threshold metric or has a highest value among a number ofsimilarity metrics associated with a particular classification, theintellectual-property services provider may determine that there is arelationship between the intellectual-property asset and the product orservice. In illustrative implementations, the intellectual-propertyservices provider may generate linguistic structures using naturallanguage processing techniques to determine similarity metrics forrespective intellectual-property assets and respective products orservices.

In various implementations, an intellectual-property services providermay generate a framework that indicates relationships betweenintellectual-property assets and products and/or services. In thesescenarios, the intellectual-property services provider may receive arequest to determine a product or service that corresponds to anintellectual-property asset. The intellectual-property services providermay receive an identifier of the product or service and then parse theframework using the identifier of the product or service to identify oneor more intellectual-property assets that the framework indicates have arelationship with the product or service. Additionally, theintellectual-property services provider may receive a request includingan identifier of an intellectual-property asset. In these cases, theintellectual-property services provider may parse the framework usingthe identifier and identify one or more products or services that theframework indicates have a relationship with the intellectual-propertyasset.

At 1408, the process 1400 includes receiving a first request todetermine a value of the intellectual-property asset. In variousimplementations, the request may be provided via one or more toolsoffered by the intellectual-property services provider. In variousimplementations, the intellectual-property services provider maygenerate one or more user interfaces by which requests for services maybe made by customers of the intellectual-property services providerand/or by representatives of the intellectual-property servicesprovider.

At 1410, the process 1400 includes identifying, based at least partly onreceiving the first request, economic data indicating revenue of anorganization that is associated with the product. The economic data mayindicate, for a given product, the amount of the organization's revenuethat is attributable to the product.

At block 1412, the process 1400 includes determining a portion of therevenue attributable to the intellectual-property asset. In illustrativeexamples, the intellectual-property services provider may determine abreadth of the intellectual-property asset with respect to the breadthof additional intellectual-property assets, such asintellectual-property assets included in a same technology category asthe intellectual-property asset. In these situations, theintellectual-property services provider may determine the portion ofrevenue of the product or service to attribute to theintellectual-property asset based at least partly on the breadth of theintellectual-property asset relative to the breadth of the additionalintellectual-property assets. A higher relative breadth score of theintellectual-property asset with respect to the additionalintellectual-property assets may cause the intellectual-propertyservices provider to apportion a larger amount of the revenue of theproduct or service to the intellectual-property asset in relation to anamount of the revenue of the product or service attributed to theintellectual-property asset in situations where the relative breadth ofthe intellectual-property asset is lower.

At 1414, the process 1400 includes determining, based at least partly onthe portion of the revenue, a measure of value of theintellectual-property asset. For example, the portion of the revenueattributable to the intellectual-property may be utilized as a factor todetermine an overall value of the asset, in addition to, in examples,revenues of other products attributable to the asset and/orcharacteristics of the asset, such as breadth, coverage, and/or exposurefactors.

At 1416, the process 1400 includes receiving a second request todetermine at least one of: a first exposure value representing loss ofcoverage with respect to the intellectual-property asset; or a secondexposure value representing a litigation event with respect to theintellectual-property asset.

At 1418, the process 1400 includes determining, based at least in parton receiving the second request, at least one of the first exposurevalue or the second exposure value. The exposure value associated withthe intellectual-property asset may be based on a probability of alitigation event occurring with respect to the intellectual-propertyasset. In additional implementations, the exposure value associated withthe intellectual-property asset may correspond to a probability that thescope of the intellectual-property asset may be reduced. In furtherimplementations, the amount of exposure associated with theintellectual-property asset may correspond to a probability that theintellectual-property asset may be invalidated in whole or in part. Inillustrative examples, the higher the amount of exposure related to theintellectual-property asset, the higher the discount applied to theportion of the revenue of the product or service attributed to theintellectual-property asset. In situations where theintellectual-property asset is a trade secret, the intellectual-propertyservices provider may determine a discount to apply to the portion ofthe revenue of the product or service attributed to theintellectual-property asset based on probability of theft of the tradesecret.

At 1420, the process 1400 includes causing display, via one or more userinterfaces, of an indicator of the measure of value of the product andthe at least one of the first exposure value or the second exposurevalue. For example, the measure of value of the intellectual-propertyasset may be determined using the revenue of the product or servicereceived by one or more organizations via sales of the product orservice over a period of time and the portion of revenue of the productor service attributed to the intellectual-property asset. In particularimplementations, the measure of value may be updated. For example, asthe intellectual-property services provider obtains updated revenueinformation for the product or service, the intellectual-propertyservices provider may update the measure of value for theintellectual-property asset based on the updated revenue. Additionally,the intellectual-property services provider may obtain information thatmay be used to update the discount applied to the portion of the revenueof the product or service to attribute to the intellectual-propertyasset and the intellectual-property service system may correspondingupdate the measure of value based on the modified discount to apply. Incertain implementation, the intellectual-property services provider mayobtain feedback indicating an accuracy of the measure of value andmodify the measure of value based on the feedback.

In various implementations, the measure of value for theintellectual-property asset may be based on a type of valuation for theintellectual-property asset. To illustrate, a first measure of value maybe determined when the intellectual-property asset is being valued aspart of a sale of the intellectual-property asset and a second measureof value may be determined when the intellectual-property asset is beingvalued as collateral for a loan. In other examples, a third measure ofvalue may be determined when the intellectual-property asset is beingvalued as part of a sale of an organization or a merger of anorganization that holds the legal rights to enforce theintellectual-property asset.

In particular implementations, additional services may be provided bythe intellectual-property services provider. For example, theintellectual-property services provider may receive a request toidentify a number of intellectual-property assets of an organizationthat are associated with a particular technology group. In otherexamples, the intellectual-property services provider may receiverequests to determine one or more risks corresponding to theintellectual-property services provider. In additional examples, theintellectual-property services provider may receive a request toidentify one or more organizations that have intellectual property in aparticular technology group or in a particular classification of asystem of classifications. In illustrative examples, theintellectual-property services provider may utilize a frameworkindicating relationships between intellectual-property assets andproducts or services to provide response to the requests. In varioussituations, the intellectual-property services provider may obtainidentifiers of intellectual-property assets, identifiers oforganizations, identifiers of products or services, identifiers oftechnology groups, or combinations thereof to utilize to parse theframework and provide responses to the requests for services. Inparticular illustrative scenarios, the various identifiers may includealphanumeric strings that include a series of characters. In additionalimplementations, the requests for services may include keywords that theintellectual-property services provider may utilize to parse theframework and generate the responses to the requests for services.

Furthermore, the foregoing is merely illustrative of the principles ofthis disclosure and various modifications can be made by those skilledin the art without departing from the scope of this disclosure. Theabove described examples are presented for purposes of illustration andnot of limitation. The present disclosure also can take many forms otherthan those explicitly described herein. Accordingly, it is emphasizedthat this disclosure is not limited to the explicitly disclosed methods,systems, and apparatuses, but is intended to include variations to andmodifications thereof, which are within the spirit of the followingclaims.

As a further example, variations of apparatus or process parameters(e.g., dimensions, configurations, components, process step order, etc.)can be made to further optimize the provided structures, devices andmethods, as shown and described herein. In any event, the structures anddevices, as well as the associated methods, described herein have manyapplications. Therefore, the disclosed subject matter should not belimited to any single example described herein, but rather should beconstrued in breadth and scope in accordance with the appended claims.

What is claimed is:
 1. A method comprising: generating a classificationsystem that includes classifications, individual ones of theclassifications corresponding to a technology group; receivinginformation about a product offered for acquisition by an organization,the information obtained from at least one of: a datastore of theorganization; a website of the organization; or input via a userinterface; determining, based at least in part on the information, afirst feature of the product; determining that the product correspondsto a classification of the classifications based at least partly on thefirst feature corresponding to a reference feature associated with theclassification; identifying an intellectual-property asset associatedwith the organization; determining a second feature of theintellectual-property asset; and determining that theintellectual-property asset corresponds to the classification based atleast partly on the second feature of the intellectual-property assetcorresponding to the reference feature associated with theclassification.
 2. The method of claim 1, further comprising:determining first words to associate with the classification;determining second words included in the information; determining thatat least a threshold number of words of the second words are included inthe first words; and wherein determining that the product corresponds tothe classification comprises determining that the product corresponds tothe classification based at least in part on the threshold number ofwords of the second words being included in the first words.
 3. Themethod of claim 1, further comprising: determining a physical feature ofthe product, the physical feature associated with a first word;determining a technical feature of the product, the technical featureassociated with a second word; and wherein the first feature correspondsto the physical feature or the technical feature.
 4. The method of claim3, further comprising: determining that at least one of the first wordor the second word is associated with the classification; and whereindetermining that the product corresponds to the classification comprisesdetermining that the product corresponds to the classification based atleast in part on the at least one of the first word or the second wordbeing associated with the classification.
 5. The method of claim 3,further comprising: determining that at least one of the first word orthe second word is associated with the intellectual-property asset; anddetermining that the product is associated with theintellectual-property asset based at least in part on the at least oneof the first word or the second word being associated with theintellectual-property asset.
 6. The method of claim 1, furthercomprising: generating a first model configured to determine a firstprobability that individual ones of the products correspond toindividual ones of the classifications; and generating a second modelconfigured to determine a second probability that individual ones of theintellectual-properly assets correspond to the individual ones of theclassifications.
 7. The method of claim 6, further comprising: sending arequest for feedback related to the classification system; receivinginput data indicating that the product does not correspond to theclassification; and training the first model based at least in part onthe input data.
 8. The method of claim 6, further comprising: sending arequest for feedback related to the classification system; receivinginput data indicating that the intellectual-property asset does notcorrespond to the classification; and training the second model based atleast in part on the input data.
 9. A system comprising: one or moreprocessors; and one or more computer-readable media storing instructionsexecutable by the one or more processors, wherein the instructions, whenexecuted by the one or more processors, cause the one or more processorsto perform operations comprising: generating a model configured todetermine classifications of at least one of products or services;receiving information about at least one of a product or a serviceoffered by an organization; identifying a word associated with the atleast one of the product or the service included in the information;determining, based at least partly on the word and utilizing the model,a probability that the at least one of the product or the servicecorresponds to a classification of the classifications; determining thatthe at least one of the product or the service corresponds to theclassification based at least partly on the probability; identifying anintellectual-property asset; determining a feature of theintellectual-property asset; and determining, utilizing the model, thatthe intellectual-property asset corresponds to the classification basedat least partly on the feature corresponding to the word.
 10. The systemof claim 9, wherein the probability comprises a first probability, theclassification comprises a first classification, and the operationsfurther comprise: determining, based at least partly on the word andutilizing the model, a second probability that the at least one of theproduct or the service corresponds to a second classification of theclassifications, the first probability being greater than the secondprobability; and wherein determining that the at least one of theproduct or the service corresponds to the first classification comprisesdetermining that the at least one of the product or the servicecorresponds to the first classification based at least in part on thefirst probability being greater than the second probability.
 11. Thesystem of claim 9, the operations further comprising: receiving inputdata indicating that the at least one of the product or the service doesnot correspond to the classification; and training the model based atleast partly on the input data.
 12. The system of claim 9, wherein theprobability is based at least partly on determining that a first numberof words included in the information corresponds to a second number ofwords of the classification.
 13. The system of claim 9, the operationsfurther comprising: determining a feature of the product based at leastpartly on words included in the information corresponding to wordsincluded in a library of features; and wherein: the classification isassociated with features; and the probability is based at least partlyon the feature of the product being included in the features of theclassification.
 14. The system of claim 9, wherein the word comprises afirst word, the classification is associated with words, and theoperations further comprise: determining that a second word included inthe information associated with the at least one of the product or theservice is included in the words; and wherein the probability is basedat least partly on the second word being included in the words.
 15. Thesystem of claim 14, the operations further comprising: determining aproximity associated with the first word and the second word, theproximity based at least in part on at least one of: a number ofintervening words between the first word and the second word; the secondword being in a same sentence as the first word; or the second wordbeing in a different sentence than the first word; and wherein theprobability is based at least in part on the proximity.
 16. A methodcomprising: generating a model configured to determine classificationsof individual ones of intellectual-property assets; receiving anintellectual-property asset; determining a word included in theintellectual-property asset; determining, based at least partly on theword and utilizing the model, a probability that at least a portion ofthe intellectual-property asset corresponds to a classification of theclassifications; determining that the at least the portion of theintellectual-property asset correspond to the classification based atleast partly on the probability; receiving information associated withat least one of a product or a service; identifying a feature of theinformation; and determining, utilizing the model, that the at least oneof the product or the service corresponds to the classification based atleast partly on the feature corresponding to the word.
 17. The method ofclaim 16, wherein the intellectual-property asset includes a patentdocument, and the method further comprises: identifying a claim of thepatent document; determining words included in the claim; determiningthat a first number of the words included in the claim correspond to asecond number of words of the classification; and wherein theprobability is based at least partly on the first number of the wordsincluded in the claim corresponding to the second number of the words ofthe classification.
 18. The method of claim 16, wherein the modelcomprises a first model, the probability comprises a first probability;and the method further comprises: generating a second model configuredto identify products that correspond to the intellectual-propertyassets; identifying a product included in the classification;determining, based at least in part on the second model, a secondprobability that the product corresponds to the intellectual-propertyasset; and determining, based at least partly on the second probability,that the product corresponds to the intellectual-property asset.
 19. Themethod of claim 16, wherein the intellectual-property asset includes atrademark document, and the method further comprises: identifying adescription of goods and services of the trademark document; determiningwords included in the description of goods and services; determiningthat a first number of the word included in the description of the goodsand the services corresponds to a second number of words of theclassification; and wherein the probability is based at least partly onthe first number of the words included in the description of the goodsand the services corresponds to the second number of the words of theclassification.
 20. The method of claim 16, further comprising:receiving input data indicating that at least a portion of theintellectual-property asset is unassociated with the classification; andtraining the model based at least partly on the input data.